Seal Phase4AE same-precision GPU numerical gate and audit limits
This commit is contained in:
Digital Drosophila Archive
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{
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"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
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"author_path": "/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py",
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"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
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"case_arrays_sha256": "f3a900e9032754619549005131283cbaf87fdaa9093887a90a295589dac590fb",
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"case_spec_sha256": "90850d6968a7832e415d0966b57e216a65f5ba8f63e488e497148b6525806d79",
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"common_interface_preserved": true,
|
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"learning": false,
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"original_poi_interface_adopted": false,
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"precision": "float32",
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"spec": {
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"description": "256-source signed fan-in, labelled duplicate/loop; refractory requests and external writes.",
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"dt_ms": 0.1,
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"duration_ms": 60.0,
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"model": {
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"dt_ms": 0.1,
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"membrane_tau_ms": 20.0,
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"refractory_ms": 2.2,
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"reset_potential_mv": -52.0,
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"resting_potential_mv": -52.0,
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"synaptic_delay_ms": 1.8,
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"synaptic_tau_ms": 5.0,
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"threshold_mv": -45.0,
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"w_syn_mv_per_anatomical_synapse": 0.275
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},
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"name": "fanin_bursts",
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"selection": null,
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"steps": 600,
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"trace_queue": true,
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"validate_queue_proxy": true
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},
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"trace_queue": true,
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"validate_queue_proxy": true,
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"whole_brain": false
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}
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+72
@@ -0,0 +1,72 @@
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{
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"c0": -0.00025935108198051754,
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"cg": 0.004937935295309027,
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"cv": 0.9950124791926823,
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"gd": 0.9801986733067553,
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"metadata": {
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"constant_values_are_double": true,
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"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
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"g_expression": "gd * old_g",
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"hex": {
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"c0": "-0x1.0ff306a505980p-12",
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"cg": "0x1.439cce9a65d5bp-8",
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"cv": "0x1.fd7246927d28bp-1",
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"gd": "0x1.f5dc99badec5bp-1",
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"reset": "-0x1.a9fbe76c8b43ap-5",
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"seed_volt": "-0x1.6872b020c49bap-5",
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"threshold": "-0x1.70a3d70a3d70ap-5"
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},
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"names": {
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"c0": "_lio_8",
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"cg": "_lio_9",
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"cv": "_lio_10",
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"gd": "_lio_2"
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},
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"provenance": {
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"c0": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_8",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"cg": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_9",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"cv": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_10",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"gd": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_2",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"reset": {
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"code_object": "default_neurons_spike_resetter_codeobject_2",
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"name": "v_rst",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"seed_volt": {
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"source": "common request interface: unchanged v_th + 1 mV"
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},
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"threshold": {
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"code_object": "default_neurons_spike_thresholder_codeobject_2",
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"name": "v_th",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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}
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},
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"units": "SI volts and dimensionless coefficients",
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"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
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},
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"reset": -0.052000000000000005,
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"seed_volt": -0.044,
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"threshold": -0.045
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}
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+14
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{
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"backend": "Brian2 numpy runtime",
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"packages": {
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"brian2": "2.5.1",
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"cython": "0.29.33",
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"numpy": "1.22.3",
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"pandas": "1.4.3",
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"pyarrow": "11.0.0",
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"scipy": "1.8.1",
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"sympy": "1.12"
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},
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"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
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"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]"
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}
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+72
@@ -0,0 +1,72 @@
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{
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"c0": -0.00025935108198051754,
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"cg": 0.004937935295309027,
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"cv": 0.9950124791926823,
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"gd": 0.9801986733067553,
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"metadata": {
|
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"constant_values_are_double": true,
|
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"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
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"g_expression": "gd * old_g",
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"hex": {
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"c0": "-0x1.0ff306a505980p-12",
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"cg": "0x1.439cce9a65d5bp-8",
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"cv": "0x1.fd7246927d28bp-1",
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"gd": "0x1.f5dc99badec5bp-1",
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"reset": "-0x1.a9fbe76c8b43ap-5",
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"seed_volt": "-0x1.6872b020c49bap-5",
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"threshold": "-0x1.70a3d70a3d70ap-5"
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},
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"names": {
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"c0": "_lio_8",
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"cg": "_lio_9",
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"cv": "_lio_10",
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"gd": "_lio_2"
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},
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"provenance": {
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"c0": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_8",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"cg": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_9",
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||||
"namespace_dtype": "float64",
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||||
"source": "CodeObject.namespace"
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},
|
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"cv": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
|
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"name": "_lio_10",
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||||
"namespace_dtype": "float64",
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||||
"source": "CodeObject.namespace"
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||||
},
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"gd": {
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"code_object": "default_neurons_stateupdater_codeobject_2",
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"name": "_lio_2",
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"namespace_dtype": "float64",
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||||
"source": "CodeObject.namespace"
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},
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"reset": {
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"code_object": "default_neurons_spike_resetter_codeobject_2",
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"name": "v_rst",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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},
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"seed_volt": {
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"source": "common request interface: unchanged v_th + 1 mV"
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},
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"threshold": {
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"code_object": "default_neurons_spike_thresholder_codeobject_2",
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"name": "v_th",
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"namespace_dtype": "float64",
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"source": "CodeObject.namespace"
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}
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},
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"units": "SI volts and dimensionless coefficients",
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"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
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},
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"reset": -0.052000000000000005,
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"seed_volt": -0.044,
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"threshold": -0.045
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}
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+162
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[
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{
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"active": true,
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"code_objects": [],
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"name": "default_neurons",
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"order": 0,
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"type": "NeuronGroup",
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"when": "start"
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||||
},
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{
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"active": true,
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"code_objects": [],
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||||
"name": "default_synapses",
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"order": 0,
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"type": "Synapses",
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"when": "start"
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},
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{
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"active": true,
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"code_objects": [],
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"name": "zz_external_arrivals",
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"order": 0,
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"type": "Synapses",
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"when": "start"
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},
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{
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"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
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"active": true,
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"code_objects": [
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||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
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"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+258
@@ -0,0 +1,258 @@
|
||||
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|
||||
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|
||||
|
+162
@@ -0,0 +1,162 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "d8163907696aec1bf853ecccbe301c50efb4abd5b1c76b9a3dfa34f0e9e36c20",
|
||||
"connectivity_sha256": "87dfbd207929725101477715d0684af71971d93cb279c3e195704b2b939b3f86",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float32",
|
||||
"queue_trace_enabled": true,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 600
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"accepted_due_events": 1285,
|
||||
"advance_calls": 600,
|
||||
"all_due_events_preserved": true,
|
||||
"due_events": 1545,
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_2"
|
||||
],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 600,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": true
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+-------------------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nzz_external_sources (SpikeGeneratorGroup) | zz_external_sources (SpikeGeneratorGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \nzz_external_arrivals_pre (SynapticPathway) | zz_external_arrivals (Synapses) | 100. us (every step) | synapses | 0 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes \nzz_oracle_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"arrival_arrays_excluded": "An unwrapped queue is deliberately not observed",
|
||||
"exact_arrays": {
|
||||
"applicable_count": true,
|
||||
"coefficients": true,
|
||||
"counts": true,
|
||||
"external_accepted": true,
|
||||
"external_step": true,
|
||||
"external_target": true,
|
||||
"external_weights": true,
|
||||
"fired": true,
|
||||
"g": true,
|
||||
"history": true,
|
||||
"initial_g": true,
|
||||
"initial_v": true,
|
||||
"local_idx": true,
|
||||
"neuron_idx": true,
|
||||
"nodes": true,
|
||||
"not_refractory": true,
|
||||
"ref": true,
|
||||
"ref_history": true,
|
||||
"rejected_count": true,
|
||||
"request_accepted": true,
|
||||
"request_attempted": true,
|
||||
"request_count": true,
|
||||
"request_rejected": true,
|
||||
"step": true,
|
||||
"v": true,
|
||||
"weights": true
|
||||
},
|
||||
"pass": true,
|
||||
"performed": true
|
||||
}
|
||||
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"applicable_requests": 1537,
|
||||
"coefficient_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/fanin_bursts-float32/coefficients.json",
|
||||
"complete": true,
|
||||
"due_arrivals": 1545,
|
||||
"edges": 258,
|
||||
"name": "fanin_bursts",
|
||||
"nodes": 257,
|
||||
"precision": "float32",
|
||||
"queue_proxy_validated": true,
|
||||
"queue_trace_enabled": true,
|
||||
"rejected_requests": 512,
|
||||
"requests": 2049,
|
||||
"source_unchanged": true,
|
||||
"spikes": 1539,
|
||||
"state_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/fanin_bursts-float32/state.npz",
|
||||
"steps": 600
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_3",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_3",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+154
@@ -0,0 +1,154 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+258
@@ -0,0 +1,258 @@
|
||||
global_neuron_idx,local_index
|
||||
-257,0
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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+154
@@ -0,0 +1,154 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "d8163907696aec1bf853ecccbe301c50efb4abd5b1c76b9a3dfa34f0e9e36c20",
|
||||
"connectivity_sha256": "87dfbd207929725101477715d0684af71971d93cb279c3e195704b2b939b3f86",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float32",
|
||||
"queue_trace_enabled": false,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 600
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"accepted_due_events": 0,
|
||||
"advance_calls": 0,
|
||||
"all_due_events_preserved": null,
|
||||
"due_events": 0,
|
||||
"installations": [],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 0,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": false
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+-------------------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nzz_external_sources (SpikeGeneratorGroup) | zz_external_sources (SpikeGeneratorGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \nzz_external_arrivals_pre (SynapticPathway) | zz_external_arrivals (Synapses) | 100. us (every step) | synapses | 0 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_path": "/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"case_arrays_sha256": "f3a900e9032754619549005131283cbaf87fdaa9093887a90a295589dac590fb",
|
||||
"case_spec_sha256": "90850d6968a7832e415d0966b57e216a65f5ba8f63e488e497148b6525806d79",
|
||||
"common_interface_preserved": true,
|
||||
"learning": false,
|
||||
"original_poi_interface_adopted": false,
|
||||
"precision": "float64",
|
||||
"spec": {
|
||||
"description": "256-source signed fan-in, labelled duplicate/loop; refractory requests and external writes.",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 60.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"name": "fanin_bursts",
|
||||
"selection": null,
|
||||
"steps": 600,
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true
|
||||
},
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true,
|
||||
"whole_brain": false
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_1",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_1",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "Brian2 numpy runtime",
|
||||
"packages": {
|
||||
"brian2": "2.5.1",
|
||||
"cython": "0.29.33",
|
||||
"numpy": "1.22.3",
|
||||
"pandas": "1.4.3",
|
||||
"pyarrow": "11.0.0",
|
||||
"scipy": "1.8.1",
|
||||
"sympy": "1.12"
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]"
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_1",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_1",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_1",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+162
@@ -0,0 +1,162 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+258
@@ -0,0 +1,258 @@
|
||||
global_neuron_idx,local_index
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
|
+162
@@ -0,0 +1,162 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "d8163907696aec1bf853ecccbe301c50efb4abd5b1c76b9a3dfa34f0e9e36c20",
|
||||
"connectivity_sha256": "87dfbd207929725101477715d0684af71971d93cb279c3e195704b2b939b3f86",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float64",
|
||||
"queue_trace_enabled": true,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 600
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"accepted_due_events": 1285,
|
||||
"advance_calls": 600,
|
||||
"all_due_events_preserved": true,
|
||||
"due_events": 1545,
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 600,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": true
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+-------------------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nzz_external_sources (SpikeGeneratorGroup) | zz_external_sources (SpikeGeneratorGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \nzz_external_arrivals_pre (SynapticPathway) | zz_external_arrivals (Synapses) | 100. us (every step) | synapses | 0 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes \nzz_oracle_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"arrival_arrays_excluded": "An unwrapped queue is deliberately not observed",
|
||||
"exact_arrays": {
|
||||
"applicable_count": true,
|
||||
"coefficients": true,
|
||||
"counts": true,
|
||||
"external_accepted": true,
|
||||
"external_step": true,
|
||||
"external_target": true,
|
||||
"external_weights": true,
|
||||
"fired": true,
|
||||
"g": true,
|
||||
"history": true,
|
||||
"initial_g": true,
|
||||
"initial_v": true,
|
||||
"local_idx": true,
|
||||
"neuron_idx": true,
|
||||
"nodes": true,
|
||||
"not_refractory": true,
|
||||
"ref": true,
|
||||
"ref_history": true,
|
||||
"rejected_count": true,
|
||||
"request_accepted": true,
|
||||
"request_attempted": true,
|
||||
"request_count": true,
|
||||
"request_rejected": true,
|
||||
"step": true,
|
||||
"v": true,
|
||||
"weights": true
|
||||
},
|
||||
"pass": true,
|
||||
"performed": true
|
||||
}
|
||||
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"applicable_requests": 1537,
|
||||
"coefficient_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/fanin_bursts-float64/coefficients.json",
|
||||
"complete": true,
|
||||
"due_arrivals": 1545,
|
||||
"edges": 258,
|
||||
"name": "fanin_bursts",
|
||||
"nodes": 257,
|
||||
"precision": "float64",
|
||||
"queue_proxy_validated": true,
|
||||
"queue_trace_enabled": true,
|
||||
"rejected_requests": 512,
|
||||
"requests": 2049,
|
||||
"source_unchanged": true,
|
||||
"spikes": 1539,
|
||||
"state_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/fanin_bursts-float64/state.npz",
|
||||
"steps": 600
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_2",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_2",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_2",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_2",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_2",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_2",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+154
@@ -0,0 +1,154 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+258
@@ -0,0 +1,258 @@
|
||||
global_neuron_idx,local_index
|
||||
-257,0
|
||||
-256,1
|
||||
-255,2
|
||||
-254,3
|
||||
-253,4
|
||||
-252,5
|
||||
-251,6
|
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+154
@@ -0,0 +1,154 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_external_arrivals",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _the_period = _array_zz_external_sources__period_bins[0]\n _timebin = _array_defaultclock_timestep[0]\n _n_spikes = 0\n _lastindex_before = _array_zz_external_sources__lastindex[0]\n if _the_period > 0:\n _timebin %= _the_period\n # If there is a periodicity in the SpikeGenerator, we need to reset the\n # lastindex when the period has passed\n if _lastindex_before > 0 and _array_zz_external_sources__timebins[_lastindex_before - 1] >= _timebin:\n _lastindex_before = 0\n _n_spikes = _numpy.searchsorted(_array_zz_external_sources__timebins[_lastindex_before:],\n _timebin, side='right')\n _array_zz_external_sources__lastindex[0] = _lastindex_before + _n_spikes\n _indices = _array_zz_external_sources_neuron_index[_lastindex_before:_lastindex_before+_n_spikes]\n _array_zz_external_sources__spikespace[:_n_spikes] = _indices\n _array_zz_external_sources__spikespace[-1] = _n_spikes\n",
|
||||
"name": "zz_external_sources_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_sources",
|
||||
"order": 0,
|
||||
"type": "SpikeGeneratorGroup",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g_post += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "zz_external_arrivals_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g_post += w\n _postsynaptic_idx = _array_zz_external_arrivals__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_zz_external_arrivals_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "zz_external_arrivals_pre_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "zz_external_arrivals_pre",
|
||||
"order": 0,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_2"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "d8163907696aec1bf853ecccbe301c50efb4abd5b1c76b9a3dfa34f0e9e36c20",
|
||||
"connectivity_sha256": "87dfbd207929725101477715d0684af71971d93cb279c3e195704b2b939b3f86",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float64",
|
||||
"queue_trace_enabled": false,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 600
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"accepted_due_events": 0,
|
||||
"advance_calls": 0,
|
||||
"all_due_events_preserved": null,
|
||||
"due_events": 0,
|
||||
"installations": [],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 0,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": false
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+-------------------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nzz_external_sources (SpikeGeneratorGroup) | zz_external_sources (SpikeGeneratorGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \nzz_external_arrivals_pre (SynapticPathway) | zz_external_arrivals (Synapses) | 100. us (every step) | synapses | 0 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_path": "/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"case_arrays_sha256": "02ab938969b4369b1e08bc4a82e8c5fe68683f7318baf5eb052098d63fab0302",
|
||||
"case_spec_sha256": "eab2c18f120fd3e9f067f489cee0a4c07477607facf3094083d3e3fa60bfd1ef",
|
||||
"common_interface_preserved": true,
|
||||
"learning": false,
|
||||
"original_poi_interface_adopted": false,
|
||||
"precision": "float32",
|
||||
"spec": {
|
||||
"description": "Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 50.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"name": "highest_indegree",
|
||||
"selection": {
|
||||
"global_in_degree": 11526,
|
||||
"negative_in_edges": 1403,
|
||||
"positive_in_edges": 10076,
|
||||
"stimulated_neuron_idx": [
|
||||
494,
|
||||
775,
|
||||
1572,
|
||||
61804,
|
||||
62257,
|
||||
62700,
|
||||
62795,
|
||||
63041,
|
||||
65262,
|
||||
66225,
|
||||
66237,
|
||||
67241,
|
||||
67324,
|
||||
68856,
|
||||
70626,
|
||||
137444
|
||||
],
|
||||
"target_body_id": 10009,
|
||||
"target_neuron_idx": 5
|
||||
},
|
||||
"steps": 500,
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true
|
||||
},
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true,
|
||||
"whole_brain": false
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_4",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_4",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "Brian2 numpy runtime",
|
||||
"packages": {
|
||||
"brian2": "2.5.1",
|
||||
"cython": "0.29.33",
|
||||
"numpy": "1.22.3",
|
||||
"pandas": "1.4.3",
|
||||
"pyarrow": "11.0.0",
|
||||
"scipy": "1.8.1",
|
||||
"sympy": "1.12"
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]"
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_4",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_4",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+129
@@ -0,0 +1,129 @@
|
||||
global_neuron_idx,local_index
|
||||
5,0
|
||||
21,1
|
||||
494,2
|
||||
775,3
|
||||
1572,4
|
||||
52515,5
|
||||
57252,6
|
||||
59937,7
|
||||
60155,8
|
||||
60450,9
|
||||
60737,10
|
||||
61164,11
|
||||
61387,12
|
||||
61721,13
|
||||
61804,14
|
||||
61941,15
|
||||
62257,16
|
||||
62700,17
|
||||
62734,18
|
||||
62795,19
|
||||
62888,20
|
||||
63041,21
|
||||
63106,22
|
||||
63173,23
|
||||
63287,24
|
||||
63339,25
|
||||
63918,26
|
||||
64210,27
|
||||
64233,28
|
||||
64443,29
|
||||
64514,30
|
||||
65001,31
|
||||
65235,32
|
||||
65262,33
|
||||
65374,34
|
||||
65565,35
|
||||
65691,36
|
||||
65742,37
|
||||
66003,38
|
||||
66127,39
|
||||
66225,40
|
||||
66237,41
|
||||
66541,42
|
||||
66598,43
|
||||
66843,44
|
||||
66958,45
|
||||
67072,46
|
||||
67135,47
|
||||
67241,48
|
||||
67284,49
|
||||
67324,50
|
||||
67373,51
|
||||
67453,52
|
||||
67483,53
|
||||
67537,54
|
||||
67594,55
|
||||
68056,56
|
||||
68074,57
|
||||
68192,58
|
||||
68199,59
|
||||
68355,60
|
||||
68543,61
|
||||
68617,62
|
||||
68656,63
|
||||
68838,64
|
||||
68856,65
|
||||
68869,66
|
||||
69139,67
|
||||
69415,68
|
||||
69516,69
|
||||
69540,70
|
||||
69809,71
|
||||
69860,72
|
||||
69966,73
|
||||
69982,74
|
||||
70036,75
|
||||
70202,76
|
||||
70316,77
|
||||
70561,78
|
||||
70626,79
|
||||
70744,80
|
||||
70783,81
|
||||
70897,82
|
||||
70977,83
|
||||
70995,84
|
||||
71060,85
|
||||
71120,86
|
||||
71215,87
|
||||
71267,88
|
||||
71346,89
|
||||
71440,90
|
||||
71456,91
|
||||
71738,92
|
||||
72060,93
|
||||
72219,94
|
||||
72322,95
|
||||
72365,96
|
||||
72473,97
|
||||
72485,98
|
||||
72578,99
|
||||
73026,100
|
||||
73221,101
|
||||
73544,102
|
||||
73653,103
|
||||
73903,104
|
||||
74495,105
|
||||
74818,106
|
||||
74995,107
|
||||
75925,108
|
||||
76961,109
|
||||
77090,110
|
||||
77291,111
|
||||
77444,112
|
||||
78506,113
|
||||
78574,114
|
||||
80691,115
|
||||
85419,116
|
||||
91986,117
|
||||
97175,118
|
||||
98515,119
|
||||
101566,120
|
||||
102237,121
|
||||
104598,122
|
||||
105190,123
|
||||
105260,124
|
||||
106136,125
|
||||
137444,126
|
||||
140350,127
|
||||
|
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "e90a4d12741fbd0bd0b5fdcf447c2664f7dfcdba7930f4e9e7f05811f0b530f4",
|
||||
"connectivity_sha256": "1228ee73ad73051748dd1db62b5063c005471667fd8baacd6a9e76b134024800",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float32",
|
||||
"queue_trace_enabled": true,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 500
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"accepted_due_events": 839,
|
||||
"advance_calls": 500,
|
||||
"all_due_events_preserved": true,
|
||||
"due_events": 930,
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_4"
|
||||
],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 500,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": true
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes \nzz_oracle_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"arrival_arrays_excluded": "An unwrapped queue is deliberately not observed",
|
||||
"exact_arrays": {
|
||||
"applicable_count": true,
|
||||
"coefficients": true,
|
||||
"counts": true,
|
||||
"external_accepted": true,
|
||||
"external_step": true,
|
||||
"external_target": true,
|
||||
"external_weights": true,
|
||||
"fired": true,
|
||||
"g": true,
|
||||
"history": true,
|
||||
"initial_g": true,
|
||||
"initial_v": true,
|
||||
"local_idx": true,
|
||||
"neuron_idx": true,
|
||||
"nodes": true,
|
||||
"not_refractory": true,
|
||||
"ref": true,
|
||||
"ref_history": true,
|
||||
"rejected_count": true,
|
||||
"request_accepted": true,
|
||||
"request_attempted": true,
|
||||
"request_count": true,
|
||||
"request_rejected": true,
|
||||
"step": true,
|
||||
"v": true,
|
||||
"weights": true
|
||||
},
|
||||
"pass": true,
|
||||
"performed": true
|
||||
}
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"applicable_requests": 96,
|
||||
"coefficient_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/highest_indegree-float32/coefficients.json",
|
||||
"complete": true,
|
||||
"due_arrivals": 930,
|
||||
"edges": 320,
|
||||
"name": "highest_indegree",
|
||||
"nodes": 128,
|
||||
"precision": "float32",
|
||||
"queue_proxy_validated": true,
|
||||
"queue_trace_enabled": true,
|
||||
"rejected_requests": 0,
|
||||
"requests": 96,
|
||||
"source_unchanged": true,
|
||||
"spikes": 101,
|
||||
"state_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/highest_indegree-float32/state.npz",
|
||||
"steps": 500
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_5",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_5",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_5",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_5",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_5",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_5",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+129
@@ -0,0 +1,129 @@
|
||||
global_neuron_idx,local_index
|
||||
5,0
|
||||
21,1
|
||||
494,2
|
||||
775,3
|
||||
1572,4
|
||||
52515,5
|
||||
57252,6
|
||||
59937,7
|
||||
60155,8
|
||||
60450,9
|
||||
60737,10
|
||||
61164,11
|
||||
61387,12
|
||||
61721,13
|
||||
61804,14
|
||||
61941,15
|
||||
62257,16
|
||||
62700,17
|
||||
62734,18
|
||||
62795,19
|
||||
62888,20
|
||||
63041,21
|
||||
63106,22
|
||||
63173,23
|
||||
63287,24
|
||||
63339,25
|
||||
63918,26
|
||||
64210,27
|
||||
64233,28
|
||||
64443,29
|
||||
64514,30
|
||||
65001,31
|
||||
65235,32
|
||||
65262,33
|
||||
65374,34
|
||||
65565,35
|
||||
65691,36
|
||||
65742,37
|
||||
66003,38
|
||||
66127,39
|
||||
66225,40
|
||||
66237,41
|
||||
66541,42
|
||||
66598,43
|
||||
66843,44
|
||||
66958,45
|
||||
67072,46
|
||||
67135,47
|
||||
67241,48
|
||||
67284,49
|
||||
67324,50
|
||||
67373,51
|
||||
67453,52
|
||||
67483,53
|
||||
67537,54
|
||||
67594,55
|
||||
68056,56
|
||||
68074,57
|
||||
68192,58
|
||||
68199,59
|
||||
68355,60
|
||||
68543,61
|
||||
68617,62
|
||||
68656,63
|
||||
68838,64
|
||||
68856,65
|
||||
68869,66
|
||||
69139,67
|
||||
69415,68
|
||||
69516,69
|
||||
69540,70
|
||||
69809,71
|
||||
69860,72
|
||||
69966,73
|
||||
69982,74
|
||||
70036,75
|
||||
70202,76
|
||||
70316,77
|
||||
70561,78
|
||||
70626,79
|
||||
70744,80
|
||||
70783,81
|
||||
70897,82
|
||||
70977,83
|
||||
70995,84
|
||||
71060,85
|
||||
71120,86
|
||||
71215,87
|
||||
71267,88
|
||||
71346,89
|
||||
71440,90
|
||||
71456,91
|
||||
71738,92
|
||||
72060,93
|
||||
72219,94
|
||||
72322,95
|
||||
72365,96
|
||||
72473,97
|
||||
72485,98
|
||||
72578,99
|
||||
73026,100
|
||||
73221,101
|
||||
73544,102
|
||||
73653,103
|
||||
73903,104
|
||||
74495,105
|
||||
74818,106
|
||||
74995,107
|
||||
75925,108
|
||||
76961,109
|
||||
77090,110
|
||||
77291,111
|
||||
77444,112
|
||||
78506,113
|
||||
78574,114
|
||||
80691,115
|
||||
85419,116
|
||||
91986,117
|
||||
97175,118
|
||||
98515,119
|
||||
101566,120
|
||||
102237,121
|
||||
104598,122
|
||||
105190,123
|
||||
105260,124
|
||||
106136,125
|
||||
137444,126
|
||||
140350,127
|
||||
|
+114
@@ -0,0 +1,114 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_5"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_5"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_5"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_5"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "e90a4d12741fbd0bd0b5fdcf447c2664f7dfcdba7930f4e9e7f05811f0b530f4",
|
||||
"connectivity_sha256": "1228ee73ad73051748dd1db62b5063c005471667fd8baacd6a9e76b134024800",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float32",
|
||||
"queue_trace_enabled": false,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 500
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"accepted_due_events": 0,
|
||||
"advance_calls": 0,
|
||||
"all_due_events_preserved": null,
|
||||
"due_events": 0,
|
||||
"installations": [],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 0,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": false
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_path": "/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"case_arrays_sha256": "02ab938969b4369b1e08bc4a82e8c5fe68683f7318baf5eb052098d63fab0302",
|
||||
"case_spec_sha256": "eab2c18f120fd3e9f067f489cee0a4c07477607facf3094083d3e3fa60bfd1ef",
|
||||
"common_interface_preserved": true,
|
||||
"learning": false,
|
||||
"original_poi_interface_adopted": false,
|
||||
"precision": "float64",
|
||||
"spec": {
|
||||
"description": "Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 50.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"name": "highest_indegree",
|
||||
"selection": {
|
||||
"global_in_degree": 11526,
|
||||
"negative_in_edges": 1403,
|
||||
"positive_in_edges": 10076,
|
||||
"stimulated_neuron_idx": [
|
||||
494,
|
||||
775,
|
||||
1572,
|
||||
61804,
|
||||
62257,
|
||||
62700,
|
||||
62795,
|
||||
63041,
|
||||
65262,
|
||||
66225,
|
||||
66237,
|
||||
67241,
|
||||
67324,
|
||||
68856,
|
||||
70626,
|
||||
137444
|
||||
],
|
||||
"target_body_id": 10009,
|
||||
"target_neuron_idx": 5
|
||||
},
|
||||
"steps": 500,
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true
|
||||
},
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true,
|
||||
"whole_brain": false
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_3",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_3",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "Brian2 numpy runtime",
|
||||
"packages": {
|
||||
"brian2": "2.5.1",
|
||||
"cython": "0.29.33",
|
||||
"numpy": "1.22.3",
|
||||
"pandas": "1.4.3",
|
||||
"pyarrow": "11.0.0",
|
||||
"scipy": "1.8.1",
|
||||
"sympy": "1.12"
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]"
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_3",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_3",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_3",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+129
@@ -0,0 +1,129 @@
|
||||
global_neuron_idx,local_index
|
||||
5,0
|
||||
21,1
|
||||
494,2
|
||||
775,3
|
||||
1572,4
|
||||
52515,5
|
||||
57252,6
|
||||
59937,7
|
||||
60155,8
|
||||
60450,9
|
||||
60737,10
|
||||
61164,11
|
||||
61387,12
|
||||
61721,13
|
||||
61804,14
|
||||
61941,15
|
||||
62257,16
|
||||
62700,17
|
||||
62734,18
|
||||
62795,19
|
||||
62888,20
|
||||
63041,21
|
||||
63106,22
|
||||
63173,23
|
||||
63287,24
|
||||
63339,25
|
||||
63918,26
|
||||
64210,27
|
||||
64233,28
|
||||
64443,29
|
||||
64514,30
|
||||
65001,31
|
||||
65235,32
|
||||
65262,33
|
||||
65374,34
|
||||
65565,35
|
||||
65691,36
|
||||
65742,37
|
||||
66003,38
|
||||
66127,39
|
||||
66225,40
|
||||
66237,41
|
||||
66541,42
|
||||
66598,43
|
||||
66843,44
|
||||
66958,45
|
||||
67072,46
|
||||
67135,47
|
||||
67241,48
|
||||
67284,49
|
||||
67324,50
|
||||
67373,51
|
||||
67453,52
|
||||
67483,53
|
||||
67537,54
|
||||
67594,55
|
||||
68056,56
|
||||
68074,57
|
||||
68192,58
|
||||
68199,59
|
||||
68355,60
|
||||
68543,61
|
||||
68617,62
|
||||
68656,63
|
||||
68838,64
|
||||
68856,65
|
||||
68869,66
|
||||
69139,67
|
||||
69415,68
|
||||
69516,69
|
||||
69540,70
|
||||
69809,71
|
||||
69860,72
|
||||
69966,73
|
||||
69982,74
|
||||
70036,75
|
||||
70202,76
|
||||
70316,77
|
||||
70561,78
|
||||
70626,79
|
||||
70744,80
|
||||
70783,81
|
||||
70897,82
|
||||
70977,83
|
||||
70995,84
|
||||
71060,85
|
||||
71120,86
|
||||
71215,87
|
||||
71267,88
|
||||
71346,89
|
||||
71440,90
|
||||
71456,91
|
||||
71738,92
|
||||
72060,93
|
||||
72219,94
|
||||
72322,95
|
||||
72365,96
|
||||
72473,97
|
||||
72485,98
|
||||
72578,99
|
||||
73026,100
|
||||
73221,101
|
||||
73544,102
|
||||
73653,103
|
||||
73903,104
|
||||
74495,105
|
||||
74818,106
|
||||
74995,107
|
||||
75925,108
|
||||
76961,109
|
||||
77090,110
|
||||
77291,111
|
||||
77444,112
|
||||
78506,113
|
||||
78574,114
|
||||
80691,115
|
||||
85419,116
|
||||
91986,117
|
||||
97175,118
|
||||
98515,119
|
||||
101566,120
|
||||
102237,121
|
||||
104598,122
|
||||
105190,123
|
||||
105260,124
|
||||
106136,125
|
||||
137444,126
|
||||
140350,127
|
||||
|
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_3"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "e90a4d12741fbd0bd0b5fdcf447c2664f7dfcdba7930f4e9e7f05811f0b530f4",
|
||||
"connectivity_sha256": "1228ee73ad73051748dd1db62b5063c005471667fd8baacd6a9e76b134024800",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float64",
|
||||
"queue_trace_enabled": true,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 500
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"accepted_due_events": 839,
|
||||
"advance_calls": 500,
|
||||
"all_due_events_preserved": true,
|
||||
"due_events": 930,
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_3"
|
||||
],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 500,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": true
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes \nzz_oracle_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"arrival_arrays_excluded": "An unwrapped queue is deliberately not observed",
|
||||
"exact_arrays": {
|
||||
"applicable_count": true,
|
||||
"coefficients": true,
|
||||
"counts": true,
|
||||
"external_accepted": true,
|
||||
"external_step": true,
|
||||
"external_target": true,
|
||||
"external_weights": true,
|
||||
"fired": true,
|
||||
"g": true,
|
||||
"history": true,
|
||||
"initial_g": true,
|
||||
"initial_v": true,
|
||||
"local_idx": true,
|
||||
"neuron_idx": true,
|
||||
"nodes": true,
|
||||
"not_refractory": true,
|
||||
"ref": true,
|
||||
"ref_history": true,
|
||||
"rejected_count": true,
|
||||
"request_accepted": true,
|
||||
"request_attempted": true,
|
||||
"request_count": true,
|
||||
"request_rejected": true,
|
||||
"step": true,
|
||||
"v": true,
|
||||
"weights": true
|
||||
},
|
||||
"pass": true,
|
||||
"performed": true
|
||||
}
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"applicable_requests": 96,
|
||||
"coefficient_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/highest_indegree-float64/coefficients.json",
|
||||
"complete": true,
|
||||
"due_arrivals": 930,
|
||||
"edges": 320,
|
||||
"name": "highest_indegree",
|
||||
"nodes": 128,
|
||||
"precision": "float64",
|
||||
"queue_proxy_validated": true,
|
||||
"queue_trace_enabled": true,
|
||||
"rejected_requests": 0,
|
||||
"requests": 96,
|
||||
"source_unchanged": true,
|
||||
"spikes": 101,
|
||||
"state_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/highest_indegree-float64/state.npz",
|
||||
"steps": 500
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_4",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_4",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_4",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+129
@@ -0,0 +1,129 @@
|
||||
global_neuron_idx,local_index
|
||||
5,0
|
||||
21,1
|
||||
494,2
|
||||
775,3
|
||||
1572,4
|
||||
52515,5
|
||||
57252,6
|
||||
59937,7
|
||||
60155,8
|
||||
60450,9
|
||||
60737,10
|
||||
61164,11
|
||||
61387,12
|
||||
61721,13
|
||||
61804,14
|
||||
61941,15
|
||||
62257,16
|
||||
62700,17
|
||||
62734,18
|
||||
62795,19
|
||||
62888,20
|
||||
63041,21
|
||||
63106,22
|
||||
63173,23
|
||||
63287,24
|
||||
63339,25
|
||||
63918,26
|
||||
64210,27
|
||||
64233,28
|
||||
64443,29
|
||||
64514,30
|
||||
65001,31
|
||||
65235,32
|
||||
65262,33
|
||||
65374,34
|
||||
65565,35
|
||||
65691,36
|
||||
65742,37
|
||||
66003,38
|
||||
66127,39
|
||||
66225,40
|
||||
66237,41
|
||||
66541,42
|
||||
66598,43
|
||||
66843,44
|
||||
66958,45
|
||||
67072,46
|
||||
67135,47
|
||||
67241,48
|
||||
67284,49
|
||||
67324,50
|
||||
67373,51
|
||||
67453,52
|
||||
67483,53
|
||||
67537,54
|
||||
67594,55
|
||||
68056,56
|
||||
68074,57
|
||||
68192,58
|
||||
68199,59
|
||||
68355,60
|
||||
68543,61
|
||||
68617,62
|
||||
68656,63
|
||||
68838,64
|
||||
68856,65
|
||||
68869,66
|
||||
69139,67
|
||||
69415,68
|
||||
69516,69
|
||||
69540,70
|
||||
69809,71
|
||||
69860,72
|
||||
69966,73
|
||||
69982,74
|
||||
70036,75
|
||||
70202,76
|
||||
70316,77
|
||||
70561,78
|
||||
70626,79
|
||||
70744,80
|
||||
70783,81
|
||||
70897,82
|
||||
70977,83
|
||||
70995,84
|
||||
71060,85
|
||||
71120,86
|
||||
71215,87
|
||||
71267,88
|
||||
71346,89
|
||||
71440,90
|
||||
71456,91
|
||||
71738,92
|
||||
72060,93
|
||||
72219,94
|
||||
72322,95
|
||||
72365,96
|
||||
72473,97
|
||||
72485,98
|
||||
72578,99
|
||||
73026,100
|
||||
73221,101
|
||||
73544,102
|
||||
73653,103
|
||||
73903,104
|
||||
74495,105
|
||||
74818,106
|
||||
74995,107
|
||||
75925,108
|
||||
76961,109
|
||||
77090,110
|
||||
77291,111
|
||||
77444,112
|
||||
78506,113
|
||||
78574,114
|
||||
80691,115
|
||||
85419,116
|
||||
91986,117
|
||||
97175,118
|
||||
98515,119
|
||||
101566,120
|
||||
102237,121
|
||||
104598,122
|
||||
105190,123
|
||||
105260,124
|
||||
106136,125
|
||||
137444,126
|
||||
140350,127
|
||||
|
+114
@@ -0,0 +1,114 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_4"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "e90a4d12741fbd0bd0b5fdcf447c2664f7dfcdba7930f4e9e7f05811f0b530f4",
|
||||
"connectivity_sha256": "1228ee73ad73051748dd1db62b5063c005471667fd8baacd6a9e76b134024800",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float64",
|
||||
"queue_trace_enabled": false,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 500
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"accepted_due_events": 0,
|
||||
"advance_calls": 0,
|
||||
"all_due_events_preserved": null,
|
||||
"due_events": 0,
|
||||
"installations": [],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 0,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": false
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
+59
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_path": "/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"case_arrays_sha256": "63fb734a967aee5f77709788ad9bd7d8783c2209c34f0e1d9aa6a0a8b7a6ecf5",
|
||||
"case_spec_sha256": "11f07dd0a3e62b6ee66373022539a68daad766dce26fa759654f0201599f6703",
|
||||
"common_interface_preserved": true,
|
||||
"learning": false,
|
||||
"original_poi_interface_adopted": false,
|
||||
"precision": "float32",
|
||||
"spec": {
|
||||
"description": "Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 50.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"name": "mixed_high_indegree",
|
||||
"selection": {
|
||||
"global_in_degree": 11526,
|
||||
"negative_in_edges": 1403,
|
||||
"positive_in_edges": 10076,
|
||||
"stimulated_neuron_idx": [
|
||||
494,
|
||||
775,
|
||||
61804,
|
||||
62257,
|
||||
62700,
|
||||
62795,
|
||||
63041,
|
||||
65262,
|
||||
66225,
|
||||
66237,
|
||||
67241,
|
||||
67324,
|
||||
67373,
|
||||
68199,
|
||||
68856,
|
||||
70626
|
||||
],
|
||||
"target_body_id": 10009,
|
||||
"target_neuron_idx": 5
|
||||
},
|
||||
"steps": 500,
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true
|
||||
},
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true,
|
||||
"whole_brain": false
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_7",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_7",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "Brian2 numpy runtime",
|
||||
"packages": {
|
||||
"brian2": "2.5.1",
|
||||
"cython": "0.29.33",
|
||||
"numpy": "1.22.3",
|
||||
"pandas": "1.4.3",
|
||||
"pyarrow": "11.0.0",
|
||||
"scipy": "1.8.1",
|
||||
"sympy": "1.12"
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]"
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_7",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_7",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_7",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+129
@@ -0,0 +1,129 @@
|
||||
global_neuron_idx,local_index
|
||||
5,0
|
||||
21,1
|
||||
44,2
|
||||
494,3
|
||||
775,4
|
||||
1572,5
|
||||
6667,6
|
||||
15460,7
|
||||
18811,8
|
||||
40937,9
|
||||
41813,10
|
||||
42005,11
|
||||
45077,12
|
||||
45449,13
|
||||
47004,14
|
||||
47028,15
|
||||
47491,16
|
||||
47754,17
|
||||
47782,18
|
||||
48155,19
|
||||
48221,20
|
||||
48947,21
|
||||
49476,22
|
||||
49706,23
|
||||
49809,24
|
||||
50350,25
|
||||
50455,26
|
||||
50561,27
|
||||
50622,28
|
||||
50734,29
|
||||
51102,30
|
||||
51157,31
|
||||
51608,32
|
||||
52222,33
|
||||
52352,34
|
||||
52494,35
|
||||
52515,36
|
||||
53063,37
|
||||
53324,38
|
||||
54010,39
|
||||
55100,40
|
||||
56539,41
|
||||
57201,42
|
||||
57252,43
|
||||
57612,44
|
||||
59164,45
|
||||
59365,46
|
||||
60182,47
|
||||
60450,48
|
||||
61164,49
|
||||
61707,50
|
||||
61721,51
|
||||
61804,52
|
||||
62257,53
|
||||
62700,54
|
||||
62734,55
|
||||
62795,56
|
||||
63041,57
|
||||
63173,58
|
||||
63287,59
|
||||
63339,60
|
||||
63918,61
|
||||
64210,62
|
||||
64233,63
|
||||
64335,64
|
||||
64514,65
|
||||
65262,66
|
||||
65848,67
|
||||
66225,68
|
||||
66237,69
|
||||
66541,70
|
||||
66598,71
|
||||
67072,72
|
||||
67241,73
|
||||
67284,74
|
||||
67324,75
|
||||
67373,76
|
||||
67453,77
|
||||
67537,78
|
||||
67594,79
|
||||
68074,80
|
||||
68199,81
|
||||
68506,82
|
||||
68617,83
|
||||
68656,84
|
||||
68785,85
|
||||
68856,86
|
||||
69139,87
|
||||
69462,88
|
||||
69516,89
|
||||
69809,90
|
||||
69860,91
|
||||
69887,92
|
||||
70316,93
|
||||
70626,94
|
||||
70662,95
|
||||
70897,96
|
||||
70959,97
|
||||
70995,98
|
||||
71060,99
|
||||
71215,100
|
||||
71267,101
|
||||
71440,102
|
||||
71456,103
|
||||
72060,104
|
||||
72275,105
|
||||
72322,106
|
||||
72365,107
|
||||
72473,108
|
||||
72485,109
|
||||
73026,110
|
||||
73544,111
|
||||
74878,112
|
||||
74995,113
|
||||
77390,114
|
||||
78506,115
|
||||
84964,116
|
||||
87611,117
|
||||
88823,118
|
||||
89271,119
|
||||
91986,120
|
||||
96731,121
|
||||
96909,122
|
||||
97175,123
|
||||
104999,124
|
||||
105260,125
|
||||
137444,126
|
||||
140350,127
|
||||
|
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_7"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_7"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_7"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_7"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "1f01bd89fa27a1fc11336336d420c1100171017febe074713369fcf51db27363",
|
||||
"connectivity_sha256": "21e51e4ecca6476d5116cfbf140b1fdd5a4a0a8ee976158f801dee8003f91ae2",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float32",
|
||||
"queue_trace_enabled": true,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 500
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"accepted_due_events": 556,
|
||||
"advance_calls": 500,
|
||||
"all_due_events_preserved": true,
|
||||
"due_events": 626,
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_7"
|
||||
],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 500,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": true
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+---------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes \nzz_oracle_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"arrival_arrays_excluded": "An unwrapped queue is deliberately not observed",
|
||||
"exact_arrays": {
|
||||
"applicable_count": true,
|
||||
"coefficients": true,
|
||||
"counts": true,
|
||||
"external_accepted": true,
|
||||
"external_step": true,
|
||||
"external_target": true,
|
||||
"external_weights": true,
|
||||
"fired": true,
|
||||
"g": true,
|
||||
"history": true,
|
||||
"initial_g": true,
|
||||
"initial_v": true,
|
||||
"local_idx": true,
|
||||
"neuron_idx": true,
|
||||
"nodes": true,
|
||||
"not_refractory": true,
|
||||
"ref": true,
|
||||
"ref_history": true,
|
||||
"rejected_count": true,
|
||||
"request_accepted": true,
|
||||
"request_attempted": true,
|
||||
"request_count": true,
|
||||
"request_rejected": true,
|
||||
"step": true,
|
||||
"v": true,
|
||||
"weights": true
|
||||
},
|
||||
"pass": true,
|
||||
"performed": true
|
||||
}
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"applicable_requests": 96,
|
||||
"coefficient_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/mixed_high_indegree-float32/coefficients.json",
|
||||
"complete": true,
|
||||
"due_arrivals": 626,
|
||||
"edges": 266,
|
||||
"name": "mixed_high_indegree",
|
||||
"nodes": 128,
|
||||
"precision": "float32",
|
||||
"queue_proxy_validated": true,
|
||||
"queue_trace_enabled": true,
|
||||
"rejected_requests": 0,
|
||||
"requests": 96,
|
||||
"source_unchanged": true,
|
||||
"spikes": 106,
|
||||
"state_file": "/data/digital-drosophila/experiments/phase4ae-author-runtime/oracle/mixed_high_indegree-float32/state.npz",
|
||||
"steps": 500
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_8",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_8",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_8",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_8",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_8",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_8",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+129
@@ -0,0 +1,129 @@
|
||||
global_neuron_idx,local_index
|
||||
5,0
|
||||
21,1
|
||||
44,2
|
||||
494,3
|
||||
775,4
|
||||
1572,5
|
||||
6667,6
|
||||
15460,7
|
||||
18811,8
|
||||
40937,9
|
||||
41813,10
|
||||
42005,11
|
||||
45077,12
|
||||
45449,13
|
||||
47004,14
|
||||
47028,15
|
||||
47491,16
|
||||
47754,17
|
||||
47782,18
|
||||
48155,19
|
||||
48221,20
|
||||
48947,21
|
||||
49476,22
|
||||
49706,23
|
||||
49809,24
|
||||
50350,25
|
||||
50455,26
|
||||
50561,27
|
||||
50622,28
|
||||
50734,29
|
||||
51102,30
|
||||
51157,31
|
||||
51608,32
|
||||
52222,33
|
||||
52352,34
|
||||
52494,35
|
||||
52515,36
|
||||
53063,37
|
||||
53324,38
|
||||
54010,39
|
||||
55100,40
|
||||
56539,41
|
||||
57201,42
|
||||
57252,43
|
||||
57612,44
|
||||
59164,45
|
||||
59365,46
|
||||
60182,47
|
||||
60450,48
|
||||
61164,49
|
||||
61707,50
|
||||
61721,51
|
||||
61804,52
|
||||
62257,53
|
||||
62700,54
|
||||
62734,55
|
||||
62795,56
|
||||
63041,57
|
||||
63173,58
|
||||
63287,59
|
||||
63339,60
|
||||
63918,61
|
||||
64210,62
|
||||
64233,63
|
||||
64335,64
|
||||
64514,65
|
||||
65262,66
|
||||
65848,67
|
||||
66225,68
|
||||
66237,69
|
||||
66541,70
|
||||
66598,71
|
||||
67072,72
|
||||
67241,73
|
||||
67284,74
|
||||
67324,75
|
||||
67373,76
|
||||
67453,77
|
||||
67537,78
|
||||
67594,79
|
||||
68074,80
|
||||
68199,81
|
||||
68506,82
|
||||
68617,83
|
||||
68656,84
|
||||
68785,85
|
||||
68856,86
|
||||
69139,87
|
||||
69462,88
|
||||
69516,89
|
||||
69809,90
|
||||
69860,91
|
||||
69887,92
|
||||
70316,93
|
||||
70626,94
|
||||
70662,95
|
||||
70897,96
|
||||
70959,97
|
||||
70995,98
|
||||
71060,99
|
||||
71215,100
|
||||
71267,101
|
||||
71440,102
|
||||
71456,103
|
||||
72060,104
|
||||
72275,105
|
||||
72322,106
|
||||
72365,107
|
||||
72473,108
|
||||
72485,109
|
||||
73026,110
|
||||
73544,111
|
||||
74878,112
|
||||
74995,113
|
||||
77390,114
|
||||
78506,115
|
||||
84964,116
|
||||
87611,117
|
||||
88823,118
|
||||
89271,119
|
||||
91986,120
|
||||
96731,121
|
||||
96909,122
|
||||
97175,123
|
||||
104999,124
|
||||
105260,125
|
||||
137444,126
|
||||
140350,127
|
||||
|
+114
@@ -0,0 +1,114 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_8"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_8"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_8"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_8"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"all_neurons_keep_original_rfc": true,
|
||||
"completeness_sha256": "1f01bd89fa27a1fc11336336d420c1100171017febe074713369fcf51db27363",
|
||||
"connectivity_sha256": "21e51e4ecca6476d5116cfbf140b1fdd5a4a0a8ee976158f801dee8003f91ae2",
|
||||
"dt_ms": 0.1,
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"case": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"case": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"case": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"case": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"case": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"case": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"case": 0.275
|
||||
}
|
||||
},
|
||||
"poisson_helper_used": false,
|
||||
"precision": "float32",
|
||||
"queue_trace_enabled": false,
|
||||
"request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
|
||||
"source_parameters_modified": false,
|
||||
"source_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"state_units": "SI volts, original dtype",
|
||||
"steps": 500
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"accepted_due_events": 0,
|
||||
"advance_calls": 0,
|
||||
"all_due_events_preserved": null,
|
||||
"due_events": 0,
|
||||
"installations": [],
|
||||
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
|
||||
"peek_calls": 0,
|
||||
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
|
||||
"trace_enabled": false
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_common_requests (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
+59
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_path": "/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"case_arrays_sha256": "63fb734a967aee5f77709788ad9bd7d8783c2209c34f0e1d9aa6a0a8b7a6ecf5",
|
||||
"case_spec_sha256": "11f07dd0a3e62b6ee66373022539a68daad766dce26fa759654f0201599f6703",
|
||||
"common_interface_preserved": true,
|
||||
"learning": false,
|
||||
"original_poi_interface_adopted": false,
|
||||
"precision": "float64",
|
||||
"spec": {
|
||||
"description": "Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 50.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"name": "mixed_high_indegree",
|
||||
"selection": {
|
||||
"global_in_degree": 11526,
|
||||
"negative_in_edges": 1403,
|
||||
"positive_in_edges": 10076,
|
||||
"stimulated_neuron_idx": [
|
||||
494,
|
||||
775,
|
||||
61804,
|
||||
62257,
|
||||
62700,
|
||||
62795,
|
||||
63041,
|
||||
65262,
|
||||
66225,
|
||||
66237,
|
||||
67241,
|
||||
67324,
|
||||
67373,
|
||||
68199,
|
||||
68856,
|
||||
70626
|
||||
],
|
||||
"target_body_id": 10009,
|
||||
"target_neuron_idx": 5
|
||||
},
|
||||
"steps": 500,
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true
|
||||
},
|
||||
"trace_queue": true,
|
||||
"validate_queue_proxy": true,
|
||||
"whole_brain": false
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_6",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_6",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "Brian2 numpy runtime",
|
||||
"packages": {
|
||||
"brian2": "2.5.1",
|
||||
"cython": "0.29.33",
|
||||
"numpy": "1.22.3",
|
||||
"pandas": "1.4.3",
|
||||
"pyarrow": "11.0.0",
|
||||
"scipy": "1.8.1",
|
||||
"sympy": "1.12"
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]"
|
||||
}
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"c0": -0.00025935108198051754,
|
||||
"cg": 0.004937935295309027,
|
||||
"cv": 0.9950124791926823,
|
||||
"gd": 0.9801986733067553,
|
||||
"metadata": {
|
||||
"constant_values_are_double": true,
|
||||
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
|
||||
"g_expression": "gd * old_g",
|
||||
"hex": {
|
||||
"c0": "-0x1.0ff306a505980p-12",
|
||||
"cg": "0x1.439cce9a65d5bp-8",
|
||||
"cv": "0x1.fd7246927d28bp-1",
|
||||
"gd": "0x1.f5dc99badec5bp-1",
|
||||
"reset": "-0x1.a9fbe76c8b43ap-5",
|
||||
"seed_volt": "-0x1.6872b020c49bap-5",
|
||||
"threshold": "-0x1.70a3d70a3d70ap-5"
|
||||
},
|
||||
"names": {
|
||||
"c0": "_lio_8",
|
||||
"cg": "_lio_9",
|
||||
"cv": "_lio_10",
|
||||
"gd": "_lio_2"
|
||||
},
|
||||
"provenance": {
|
||||
"c0": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_8",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cg": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_9",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"cv": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_10",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"gd": {
|
||||
"code_object": "default_neurons_stateupdater_codeobject_6",
|
||||
"name": "_lio_2",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"reset": {
|
||||
"code_object": "default_neurons_spike_resetter_codeobject_6",
|
||||
"name": "v_rst",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
},
|
||||
"seed_volt": {
|
||||
"source": "common request interface: unchanged v_th + 1 mV"
|
||||
},
|
||||
"threshold": {
|
||||
"code_object": "default_neurons_spike_thresholder_codeobject_6",
|
||||
"name": "v_th",
|
||||
"namespace_dtype": "float64",
|
||||
"source": "CodeObject.namespace"
|
||||
}
|
||||
},
|
||||
"units": "SI volts and dimensionless coefficients",
|
||||
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
|
||||
+122
@@ -0,0 +1,122 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n dt = _array_defaultclock_dt[0]\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_v[:] = v\n",
|
||||
"name": "default_neurons_stateupdater_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_common_requests",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n not_refractory = _array_default_neurons_not_refractory\n v = _array_default_neurons_v\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_t = _array_defaultclock_t[0]\n _source_i = _array_default_neurons_i[_idx]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n w = _array_default_synapses_w[_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_g = _array_default_neurons_g[_idx]\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_oracle_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user