Seal Phase 4AJ fixed APL timing hypothesis audit
This commit is contained in:
Digital Drosophila Archive
@@ -0,0 +1,12 @@
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{
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"KC_targets_in_case": 20,
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"base_graph_files_modified": false,
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"base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585",
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"count": 50,
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"effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406",
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"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
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"fullgraph_required_mask_count": 5845,
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"graded_targets": "only KC identities present in this LIF case",
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"ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825",
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"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
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}
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@@ -0,0 +1,39 @@
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{
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"APL_generation_steps_after_window": 0,
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"apl_delay_steps": 18,
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"applied_generated_rows": 82,
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"brain_steps_after_window": 0,
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"brain_tail_advanced": false,
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"closed_loop_branch_integrals_claimed_equal": false,
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"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
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"final_apl_state_float32": [
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0.0,
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0.0
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],
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"final_apl_state_nonzero": false,
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"final_apl_state_u32_bits": [
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0,
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0
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],
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"finite_generated_sequence_partition_exact": true,
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||||
"future_apl_decay_or_feedback_generated": false,
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"future_generated_decay_tail_included": false,
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"generated_rows": 100,
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"initial_zero_prefix": 0,
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"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
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"pending_application_tick_start": 100,
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"pending_application_tick_stop_exclusive": 118,
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"pending_file_sha256": "f4c6097555e9bca1551d23df2751afd3d8ae6914559233707de894b51c9dd200",
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"pending_generated_count": 18,
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"pending_generated_tick_start": 82,
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"pending_generated_tick_stop_exclusive": 100,
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"pending_initial_zero_prefix": 0,
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"pending_raw_sha256": "81cc5530fab22979a78429b863d208e62d9249ddff3a0a06de0edbda0c688aaf",
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"pending_shape": [
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18,
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4064
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],
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"positive_zero_warmup_exact": true,
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"steps": 100,
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"transport_shift_exact": true
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}
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+56
@@ -0,0 +1,56 @@
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{
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"command": [
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"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
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"-B",
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"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
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"--bundle-dir",
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"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
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"--dt-ms",
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"0.1"
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],
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"handshake": {
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"array_sha256": {
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"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
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"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
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"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
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"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
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},
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"decay_f32_bits": "3f7eb923",
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"dt_ms": 0.1,
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"environment": {
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"CUBLAS_WORKSPACE_CONFIG": null,
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"cuda": "13.0",
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"cuda_matmul_allow_tf32": false,
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"cudnn": 92400,
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"cudnn_allow_tf32": true,
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"cudnn_benchmark": false,
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"cudnn_deterministic": false,
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"deterministic_algorithms": false,
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"device_capability": [
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8,
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9
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],
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"device_index": 0,
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"device_name": "NVIDIA GeForce RTX 4090",
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"float32_matmul_precision": "highest",
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"numpy": "2.2.6",
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"precision_configuration_changed_by_component": false,
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"torch": "2.14.0+cu130"
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},
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"files_sha256": {
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"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
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"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
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"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
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},
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"gain_f32_bits": "3c23d70a",
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"h_units": "legacy mV",
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"initial_state": "float32 zeros",
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"n_apl": 2,
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"n_kc": 4064,
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"n_neurons": 166700,
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"ok": true,
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"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
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"protocol": "phase4ag-apl-v1"
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}
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}
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@@ -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_1",
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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_1",
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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_1",
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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_1",
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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_1",
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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_1",
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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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||||
"units": "SI volts and dimensionless coefficients",
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"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))"
|
||||
},
|
||||
"reset": -0.052000000000000005,
|
||||
"seed_volt": -0.044,
|
||||
"threshold": -0.045
|
||||
}
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@@ -0,0 +1,124 @@
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[
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||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
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"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"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",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "before_run:\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 dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n t = _array_defaultclock_t[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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "before_run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 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": "before_run:\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_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "before_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()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "before_run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "before_run:\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "before_run:\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 dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n t = _array_defaultclock_t[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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "before_run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "before_run:\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_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "before_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()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "before_run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "before_run:\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8",
|
||||
"requests.npz": "6b633838a29f5aee68f83e7fe97e95e5cf15063280a639114d404afb4dd5cbe8",
|
||||
"spec.json": "3fb352a662be441922305b4faaa8a21c2603503892181564512571e85a90d1d6"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f",
|
||||
"connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b",
|
||||
"dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6",
|
||||
"edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765",
|
||||
"nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd",
|
||||
"ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81",
|
||||
"signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471",
|
||||
"src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f",
|
||||
"weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 10.0,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures",
|
||||
"kind": "fixture",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"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": "apl_only",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"record_history": true,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"steps": 100,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
100,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
100,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
100,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
100,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": true,
|
||||
"history_shape": [
|
||||
100,
|
||||
44
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"advance_calls": 100,
|
||||
"forwarding_contract": "sealed AE/AF design",
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"limit": "Current wrapper was not independently rerun without the queue proxy",
|
||||
"new_unwrapped_repeat_performed": false,
|
||||
"original_edge_ids_not_sorted_or_filtered": true,
|
||||
"peek_calls": 100,
|
||||
"post_threshold_pre_synaptic_guard_observed": true,
|
||||
"same_peek_object_returned": true,
|
||||
"zero_weight_and_refractory_rejected_edges_retained": true
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
tick,neuron_idx,attempted,accepted,rejected
|
||||
0,502,1,1,0
|
||||
0,904,1,1,0
|
||||
|
@@ -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_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"APL_applied_sha256": "429a69417d057704b5a9364e5dc9968d7a690c83f43b096c9284be47835d4c9f",
|
||||
"APL_fast_edges_zeroed": 50,
|
||||
"APL_finite_sequence_partition_exact": true,
|
||||
"APL_pending_tail_sha256": "f4c6097555e9bca1551d23df2751afd3d8ae6914559233707de894b51c9dd200",
|
||||
"APL_precision": "float32",
|
||||
"APL_timing_audit_sha256": "432865ff2eb3abc72948b18d963ef2498651a4e0aac43a7800ebce25d0c29a24",
|
||||
"APL_transport_shift_exact": true,
|
||||
"APL_worker_exit_status": 0,
|
||||
"APL_worker_ticks": 100,
|
||||
"apl_delay_steps": 18,
|
||||
"apl_fast_edges_zeroed": 50,
|
||||
"applicable_requests": 2,
|
||||
"backend": "shared-APL-component closed-loop Brian2 oracle",
|
||||
"candidate_trace_used_as_input": false,
|
||||
"complete": true,
|
||||
"completed_ticks": 100,
|
||||
"edges": 287,
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"events_sha256": "f42e10e38960132803d2eb1c3ccf3570e7a8d6618ed2d77d60bf830bf2c4812e",
|
||||
"future_generated_decay_tail_included": false,
|
||||
"history_recorded": true,
|
||||
"independent_CPU_all_components_claim": false,
|
||||
"initial_zero_prefix": 0,
|
||||
"kc_targets_present": 20,
|
||||
"name": "apl_only",
|
||||
"no_poi": true,
|
||||
"no_training": true,
|
||||
"nodes": 44,
|
||||
"output_budget_bytes": 25769803776,
|
||||
"output_bytes_before_summary": 873811613,
|
||||
"pass_all": true,
|
||||
"pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate",
|
||||
"pending_generated_count": 18,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_tail_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"precision": "float64",
|
||||
"queue_sha256": "306299f8fb7e6901ce92c3e146566f8ce1f65138090daefbbe367f013d2eebaa",
|
||||
"ref_rejected_requests": 0,
|
||||
"requests": 2,
|
||||
"scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation",
|
||||
"single_continuous_run": true,
|
||||
"source_and_inputs_unchanged": true,
|
||||
"spikes": 2,
|
||||
"state_hashes_sha256": "e2936c94d40319270491846542963ea9439c94891008003edf2436366ee42e8e",
|
||||
"steps": 100,
|
||||
"weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406"
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"KC_targets_in_case": 20,
|
||||
"base_graph_files_modified": false,
|
||||
"base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585",
|
||||
"count": 50,
|
||||
"effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406",
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"fullgraph_required_mask_count": 5845,
|
||||
"graded_targets": "only KC identities present in this LIF case",
|
||||
"ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825",
|
||||
"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
|
||||
}
|
||||
+39
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"APL_generation_steps_after_window": 0,
|
||||
"apl_delay_steps": 18,
|
||||
"applied_generated_rows": 102,
|
||||
"brain_steps_after_window": 0,
|
||||
"brain_tail_advanced": false,
|
||||
"closed_loop_branch_integrals_claimed_equal": false,
|
||||
"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
|
||||
"final_apl_state_float32": [
|
||||
1.7472392320632935,
|
||||
0.02430599182844162
|
||||
],
|
||||
"final_apl_state_nonzero": true,
|
||||
"final_apl_state_u32_bits": [
|
||||
1071621513,
|
||||
1019682140
|
||||
],
|
||||
"finite_generated_sequence_partition_exact": true,
|
||||
"future_apl_decay_or_feedback_generated": false,
|
||||
"future_generated_decay_tail_included": false,
|
||||
"generated_rows": 120,
|
||||
"initial_zero_prefix": 0,
|
||||
"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
|
||||
"pending_application_tick_start": 120,
|
||||
"pending_application_tick_stop_exclusive": 138,
|
||||
"pending_file_sha256": "e514623eabc82bce17bc0c6d361f1c442668236676aef9caf893cb855ccb8d44",
|
||||
"pending_generated_count": 18,
|
||||
"pending_generated_tick_start": 102,
|
||||
"pending_generated_tick_stop_exclusive": 120,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_raw_sha256": "3bfbf7564b318282f98f8a3b19818328c93ca5b719afcce258d44b7b524d44f3",
|
||||
"pending_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"positive_zero_warmup_exact": true,
|
||||
"steps": 120,
|
||||
"transport_shift_exact": true
|
||||
}
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"command": [
|
||||
"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"-B",
|
||||
"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"--bundle-dir",
|
||||
"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"--dt-ms",
|
||||
"0.1"
|
||||
],
|
||||
"handshake": {
|
||||
"array_sha256": {
|
||||
"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
|
||||
"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
|
||||
"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
|
||||
},
|
||||
"decay_f32_bits": "3f7eb923",
|
||||
"dt_ms": 0.1,
|
||||
"environment": {
|
||||
"CUBLAS_WORKSPACE_CONFIG": null,
|
||||
"cuda": "13.0",
|
||||
"cuda_matmul_allow_tf32": false,
|
||||
"cudnn": 92400,
|
||||
"cudnn_allow_tf32": true,
|
||||
"cudnn_benchmark": false,
|
||||
"cudnn_deterministic": false,
|
||||
"deterministic_algorithms": false,
|
||||
"device_capability": [
|
||||
8,
|
||||
9
|
||||
],
|
||||
"device_index": 0,
|
||||
"device_name": "NVIDIA GeForce RTX 4090",
|
||||
"float32_matmul_precision": "highest",
|
||||
"numpy": "2.2.6",
|
||||
"precision_configuration_changed_by_component": false,
|
||||
"torch": "2.14.0+cu130"
|
||||
},
|
||||
"files_sha256": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"gain_f32_bits": "3c23d70a",
|
||||
"h_units": "legacy mV",
|
||||
"initial_state": "float32 zeros",
|
||||
"n_apl": 2,
|
||||
"n_kc": 4064,
|
||||
"n_neurons": 166700,
|
||||
"ok": true,
|
||||
"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
|
||||
"protocol": "phase4ag-apl-v1"
|
||||
}
|
||||
}
|
||||
+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
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "run:\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 v = _array_default_neurons_v.copy()\n g = _array_default_neurons_g.copy()\n dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n t = _array_defaultclock_t[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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\nafter_run:\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\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": "run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\nafter_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\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": "run:\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\nafter_run:\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\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\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()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\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\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": "run:\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\nafter_run:\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\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+17
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "run:\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 v = _array_default_neurons_v.copy()\n g = _array_default_neurons_g.copy()\n dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n t = _array_defaultclock_t[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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\nafter_run:\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\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": "run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\nafter_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\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "run:\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\nafter_run:\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\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nafter_run:\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()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\nafter_run:\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\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "run:\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\nafter_run:\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\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+70
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8",
|
||||
"requests.npz": "2b5c4d1491dd65ac70a2e23b8d4db9be7f0798fabff114c308a753ccb58a051f",
|
||||
"spec.json": "999fcfbed858e38f7b1f2fd9d09af8f7ec0c7754cf0d43d5ee2565e2ff4487a1"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f",
|
||||
"connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b",
|
||||
"dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6",
|
||||
"edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765",
|
||||
"nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd",
|
||||
"ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81",
|
||||
"signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471",
|
||||
"src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f",
|
||||
"weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 12.0,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures",
|
||||
"kind": "fixture",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"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": "arrival_collision",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"record_history": true,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"steps": 120,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
120,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
120,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
120,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
120,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": true,
|
||||
"history_shape": [
|
||||
120,
|
||||
44
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
+34
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"advance_calls": 120,
|
||||
"forwarding_contract": "sealed AE/AF design",
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"limit": "Current wrapper was not independently rerun without the queue proxy",
|
||||
"new_unwrapped_repeat_performed": false,
|
||||
"original_edge_ids_not_sorted_or_filtered": true,
|
||||
"peek_calls": 120,
|
||||
"post_threshold_pre_synaptic_guard_observed": true,
|
||||
"same_peek_object_returned": true,
|
||||
"zero_weight_and_refractory_rejected_edges_retained": true
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
tick,neuron_idx,attempted,accepted,rejected
|
||||
0,71,1,1,0
|
||||
0,38891,1,1,0
|
||||
18,88824,1,1,0
|
||||
21,38891,1,0,1
|
||||
|
@@ -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_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"APL_applied_sha256": "fcefb08a3c7bddb77dcc691b645168249fd202481efaf465d18e1b88331d39ee",
|
||||
"APL_fast_edges_zeroed": 50,
|
||||
"APL_finite_sequence_partition_exact": true,
|
||||
"APL_pending_tail_sha256": "e514623eabc82bce17bc0c6d361f1c442668236676aef9caf893cb855ccb8d44",
|
||||
"APL_precision": "float32",
|
||||
"APL_timing_audit_sha256": "8872f480162b16e9d13df73815348c970ffa576bfcad4d93fb5a2bb183eb5050",
|
||||
"APL_transport_shift_exact": true,
|
||||
"APL_worker_exit_status": 0,
|
||||
"APL_worker_ticks": 120,
|
||||
"apl_delay_steps": 18,
|
||||
"apl_fast_edges_zeroed": 50,
|
||||
"applicable_requests": 3,
|
||||
"backend": "shared-APL-component closed-loop Brian2 oracle",
|
||||
"candidate_trace_used_as_input": false,
|
||||
"complete": true,
|
||||
"completed_ticks": 120,
|
||||
"edges": 287,
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"events_sha256": "39136eaca57a5fc7eee6e4c41af79421a0e5931cdfba474e33949a775d5db53e",
|
||||
"future_generated_decay_tail_included": false,
|
||||
"history_recorded": true,
|
||||
"independent_CPU_all_components_claim": false,
|
||||
"initial_zero_prefix": 0,
|
||||
"kc_targets_present": 20,
|
||||
"name": "arrival_collision",
|
||||
"no_poi": true,
|
||||
"no_training": true,
|
||||
"nodes": 44,
|
||||
"output_budget_bytes": 25769803776,
|
||||
"output_bytes_before_summary": 843104430,
|
||||
"pass_all": true,
|
||||
"pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate",
|
||||
"pending_generated_count": 18,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_tail_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"precision": "float64",
|
||||
"queue_sha256": "a14e60c29eee17afa66692052d5aa7661c6269785a5672ab116b9ca096a247e0",
|
||||
"ref_rejected_requests": 1,
|
||||
"requests": 4,
|
||||
"scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation",
|
||||
"single_continuous_run": true,
|
||||
"source_and_inputs_unchanged": true,
|
||||
"spikes": 3,
|
||||
"state_hashes_sha256": "4a9c8cb7e6aafa2a5d01ec1df913d00029c01137c7282d6cb32b3494ce1e9b19",
|
||||
"steps": 120,
|
||||
"weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406"
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"KC_targets_in_case": 4064,
|
||||
"base_graph_files_modified": false,
|
||||
"base_weight_raw_bytes_sha256": "a8c19cde743bd456bcb2e988d504f805b6943d6ceb97cd241d6e1e472ab5a591",
|
||||
"count": 5845,
|
||||
"effective_weights_file_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b",
|
||||
"effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9",
|
||||
"fullgraph_required_mask_count": 5845,
|
||||
"graded_targets": "only KC identities present in this LIF case",
|
||||
"ordered_original_edge_id_sha256": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b",
|
||||
"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
|
||||
}
|
||||
+39
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"APL_generation_steps_after_window": 0,
|
||||
"apl_delay_steps": 18,
|
||||
"applied_generated_rows": 982,
|
||||
"brain_steps_after_window": 0,
|
||||
"brain_tail_advanced": false,
|
||||
"closed_loop_branch_integrals_claimed_equal": false,
|
||||
"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
|
||||
"final_apl_state_float32": [
|
||||
154.97776794433594,
|
||||
160.13018798828125
|
||||
],
|
||||
"final_apl_state_nonzero": true,
|
||||
"final_apl_state_u32_bits": [
|
||||
1125841487,
|
||||
1126179156
|
||||
],
|
||||
"finite_generated_sequence_partition_exact": true,
|
||||
"future_apl_decay_or_feedback_generated": false,
|
||||
"future_generated_decay_tail_included": false,
|
||||
"generated_rows": 1000,
|
||||
"initial_zero_prefix": 0,
|
||||
"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
|
||||
"pending_application_tick_start": 1000,
|
||||
"pending_application_tick_stop_exclusive": 1018,
|
||||
"pending_file_sha256": "2d190ab166a4c5af542629a0e09aca50f8ccdc2b756dfe21de1010075388b511",
|
||||
"pending_generated_count": 18,
|
||||
"pending_generated_tick_start": 982,
|
||||
"pending_generated_tick_stop_exclusive": 1000,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_raw_sha256": "f0deb79fd65f4e200158fffbb1d559a632acbd8cc6dccafd865e0c1790a362ca",
|
||||
"pending_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"positive_zero_warmup_exact": true,
|
||||
"steps": 1000,
|
||||
"transport_shift_exact": true
|
||||
}
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"command": [
|
||||
"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"-B",
|
||||
"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"--bundle-dir",
|
||||
"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"--dt-ms",
|
||||
"0.1"
|
||||
],
|
||||
"handshake": {
|
||||
"array_sha256": {
|
||||
"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
|
||||
"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
|
||||
"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
|
||||
},
|
||||
"decay_f32_bits": "3f7eb923",
|
||||
"dt_ms": 0.1,
|
||||
"environment": {
|
||||
"CUBLAS_WORKSPACE_CONFIG": null,
|
||||
"cuda": "13.0",
|
||||
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|
||||
"cudnn": 92400,
|
||||
"cudnn_allow_tf32": true,
|
||||
"cudnn_benchmark": false,
|
||||
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|
||||
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|
||||
"device_capability": [
|
||||
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|
||||
9
|
||||
],
|
||||
"device_index": 0,
|
||||
"device_name": "NVIDIA GeForce RTX 4090",
|
||||
"float32_matmul_precision": "highest",
|
||||
"numpy": "2.2.6",
|
||||
"precision_configuration_changed_by_component": false,
|
||||
"torch": "2.14.0+cu130"
|
||||
},
|
||||
"files_sha256": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
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|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"gain_f32_bits": "3c23d70a",
|
||||
"h_units": "legacy mV",
|
||||
"initial_state": "float32 zeros",
|
||||
"n_apl": 2,
|
||||
"n_kc": 4064,
|
||||
"n_neurons": 166700,
|
||||
"ok": true,
|
||||
"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
|
||||
"protocol": "phase4ag-apl-v1"
|
||||
}
|
||||
}
|
||||
+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
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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\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 rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n g = _array_default_neurons_g.copy()\n v = _array_default_neurons_v.copy()\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_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\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\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\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\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\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_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\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\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\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\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\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()\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\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\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\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\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+17
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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\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 rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n g = _array_default_neurons_g.copy()\n v = _array_default_neurons_v.copy()\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_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n _array_default_neurons_g[:] = g\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\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\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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\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_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\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\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\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\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\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()\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\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\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\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+77
@@ -0,0 +1,77 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"requests.npz": "d49fb00b3870c8e923c0e5fc903536c0d8985555e5052c23ebc6f963c8af5314",
|
||||
"spec.json": "8fc9e0398031edeafafb21e4fc1a869ff18a72a0331e4ef172b0163eddf3bc89"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fb9033398abb7585ae63472ced9d8c8cab787f69965dc9ce5fa409ab105bc1bc",
|
||||
"connectivity.parquet": "1362c1102b74f5552f4b01fca956574fd6252f749e31493c4a00a1bc9eab00b3",
|
||||
"dst.npy": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150",
|
||||
"edge_id.npy": "49260615ff2b6432ce3dea9ce2c7ab8f83d06c2fb7491e58e08355fcedf86a30",
|
||||
"nodes.npy": "5b511a98e8fbf8d65bbc70b93e75ecae19366f650b7842a45611bfad62ed83dc",
|
||||
"ptr.npy": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4",
|
||||
"signed_raw.npy": "f9884ccb8930b32420b27c69271df78034a8350fe5a5c9b76a63c259268efc09",
|
||||
"src.npy": "bfd625ce690fc21eb0a8b9dede831688ab73b789045ba83ddf4baeefeddf1fc3",
|
||||
"weights.npy": "462839ffc3feb8f6c278044488082b1c8e410e7f23229d6b5e29d1b7042416a1"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 100.0,
|
||||
"expected_external_events": 102.24431762695312,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/full",
|
||||
"kind": "calibration",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"mode": "matched_energy",
|
||||
"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": "cal-matched_energy-00-s51101",
|
||||
"odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N",
|
||||
"odor_index": 0,
|
||||
"odor_name": "ethyl (R)-(-)-3-hydroxybutanoate",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"rate_sum_hz": 1022.4431762695312,
|
||||
"record_history": false,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"seed": 51101,
|
||||
"steps": 1000,
|
||||
"target_count": 48,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
1000,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
1000,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
1000,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
1000,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": false,
|
||||
"history_shape": [
|
||||
1000,
|
||||
166700
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
+34
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"advance_calls": 1000,
|
||||
"forwarding_contract": "sealed AE/AF design",
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"limit": "Current wrapper was not independently rerun without the queue proxy",
|
||||
"new_unwrapped_repeat_performed": false,
|
||||
"original_edge_ids_not_sorted_or_filtered": true,
|
||||
"peek_calls": 1000,
|
||||
"post_threshold_pre_synaptic_guard_observed": true,
|
||||
"same_peek_object_returned": true,
|
||||
"zero_weight_and_refractory_rejected_edges_retained": true
|
||||
}
|
||||
+118
@@ -0,0 +1,118 @@
|
||||
tick,neuron_idx,attempted,accepted,rejected
|
||||
1,522,1,1,0
|
||||
26,2010,1,1,0
|
||||
27,7679,1,1,0
|
||||
31,4311,1,1,0
|
||||
37,7781,1,1,0
|
||||
40,2601,1,1,0
|
||||
42,3345,1,1,0
|
||||
58,1011,1,1,0
|
||||
59,2493,1,1,0
|
||||
69,5519,1,1,0
|
||||
81,7679,1,1,0
|
||||
86,3252,1,1,0
|
||||
95,1514,1,1,0
|
||||
102,3252,1,0,1
|
||||
103,12644,1,1,0
|
||||
115,130497,1,1,0
|
||||
127,2970,1,1,0
|
||||
130,4505,1,0,1
|
||||
137,2010,1,1,0
|
||||
162,2029,1,1,0
|
||||
162,130472,1,1,0
|
||||
163,2010,1,1,0
|
||||
181,522,1,1,0
|
||||
190,10769,1,1,0
|
||||
195,360,1,1,0
|
||||
198,2493,1,1,0
|
||||
199,1011,1,1,0
|
||||
241,1011,1,1,0
|
||||
247,10769,1,1,0
|
||||
248,6842,1,0,1
|
||||
252,2010,1,1,0
|
||||
253,4026,1,1,0
|
||||
259,360,1,1,0
|
||||
260,4026,1,0,1
|
||||
265,999,1,1,0
|
||||
270,3345,1,1,0
|
||||
271,2231,1,1,0
|
||||
288,1514,1,0,1
|
||||
299,3161,1,1,0
|
||||
300,522,1,0,1
|
||||
319,3252,1,0,1
|
||||
326,1514,1,1,0
|
||||
331,2601,1,1,0
|
||||
335,9975,1,1,0
|
||||
345,2586,1,1,0
|
||||
365,3202,1,0,1
|
||||
377,3252,1,0,1
|
||||
412,1514,1,1,0
|
||||
413,2493,1,1,0
|
||||
417,130753,1,1,0
|
||||
418,130753,1,0,1
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|
||||
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||||
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|
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650,2010,1,0,1
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||||
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||||
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||||
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|
||||
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||||
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|
||||
844,130753,1,0,1
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||||
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|
||||
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|
||||
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|
||||
872,1191,1,1,0
|
||||
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||||
878,1927,1,1,0
|
||||
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|
||||
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|
||||
904,130497,1,1,0
|
||||
906,1514,1,1,0
|
||||
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|
||||
942,7679,1,0,1
|
||||
951,5519,1,1,0
|
||||
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|
||||
955,2493,1,1,0
|
||||
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|
||||
961,130753,1,1,0
|
||||
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|
||||
993,14839,1,0,1
|
||||
|
+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_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+51
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"APL_applied_sha256": "228218d929370f412387eddc969694178b025186ed4d226a17e57c2d46bf49d5",
|
||||
"APL_fast_edges_zeroed": 5845,
|
||||
"APL_finite_sequence_partition_exact": true,
|
||||
"APL_pending_tail_sha256": "2d190ab166a4c5af542629a0e09aca50f8ccdc2b756dfe21de1010075388b511",
|
||||
"APL_precision": "float32",
|
||||
"APL_timing_audit_sha256": "d7b5126b8d5fee07d4bb398674a864d2c8005e5a46fc1728fbc46b0fc6180192",
|
||||
"APL_transport_shift_exact": true,
|
||||
"APL_worker_exit_status": 0,
|
||||
"APL_worker_ticks": 1000,
|
||||
"apl_delay_steps": 18,
|
||||
"apl_fast_edges_zeroed": 5845,
|
||||
"applicable_requests": 88,
|
||||
"backend": "shared-APL-component closed-loop Brian2 oracle",
|
||||
"candidate_trace_used_as_input": false,
|
||||
"complete": true,
|
||||
"completed_ticks": 1000,
|
||||
"edges": 25582938,
|
||||
"effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9",
|
||||
"events_sha256": "b18dddcb78d047d0f67e791750acc1c0efacc876756c7d6344439cd9860fc5ff",
|
||||
"future_generated_decay_tail_included": false,
|
||||
"history_recorded": false,
|
||||
"independent_CPU_all_components_claim": false,
|
||||
"initial_zero_prefix": 0,
|
||||
"kc_targets_present": 4064,
|
||||
"name": "cal-matched_energy-00-s51101",
|
||||
"no_poi": true,
|
||||
"no_training": true,
|
||||
"nodes": 166700,
|
||||
"output_budget_bytes": 25769803776,
|
||||
"output_bytes_before_summary": 2036311224,
|
||||
"pass_all": true,
|
||||
"pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate",
|
||||
"pending_generated_count": 18,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_tail_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"precision": "float64",
|
||||
"queue_sha256": "5dc31a6a6d44931f6aac9bba21560277c11376b5a5ee18a99d86c220431d1a66",
|
||||
"ref_rejected_requests": 29,
|
||||
"requests": 117,
|
||||
"scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation",
|
||||
"single_continuous_run": true,
|
||||
"source_and_inputs_unchanged": true,
|
||||
"spikes": 29368,
|
||||
"state_hashes_sha256": "818e8bdf73e841723fa60e671d18bf9f74b9025ec31e339d578845f63cb130ee",
|
||||
"steps": 1000,
|
||||
"weights_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b"
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"KC_targets_in_case": 4064,
|
||||
"base_graph_files_modified": false,
|
||||
"base_weight_raw_bytes_sha256": "a8c19cde743bd456bcb2e988d504f805b6943d6ceb97cd241d6e1e472ab5a591",
|
||||
"count": 5845,
|
||||
"effective_weights_file_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b",
|
||||
"effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9",
|
||||
"fullgraph_required_mask_count": 5845,
|
||||
"graded_targets": "only KC identities present in this LIF case",
|
||||
"ordered_original_edge_id_sha256": "78d88b017b336f9c72a230680a39a9c4e74fedeff6fbb7b9c8641f30cb15e72b",
|
||||
"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
|
||||
}
|
||||
+39
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"APL_generation_steps_after_window": 0,
|
||||
"apl_delay_steps": 18,
|
||||
"applied_generated_rows": 982,
|
||||
"brain_steps_after_window": 0,
|
||||
"brain_tail_advanced": false,
|
||||
"closed_loop_branch_integrals_claimed_equal": false,
|
||||
"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
|
||||
"final_apl_state_float32": [
|
||||
153.00985717773438,
|
||||
167.6389617919922
|
||||
],
|
||||
"final_apl_state_nonzero": true,
|
||||
"final_apl_state_u32_bits": [
|
||||
1125712518,
|
||||
1126671251
|
||||
],
|
||||
"finite_generated_sequence_partition_exact": true,
|
||||
"future_apl_decay_or_feedback_generated": false,
|
||||
"future_generated_decay_tail_included": false,
|
||||
"generated_rows": 1000,
|
||||
"initial_zero_prefix": 0,
|
||||
"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
|
||||
"pending_application_tick_start": 1000,
|
||||
"pending_application_tick_stop_exclusive": 1018,
|
||||
"pending_file_sha256": "830e2f33958a35666f61a219c72025b06970f1043971310f56f112253c6c816d",
|
||||
"pending_generated_count": 18,
|
||||
"pending_generated_tick_start": 982,
|
||||
"pending_generated_tick_stop_exclusive": 1000,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_raw_sha256": "b00649dc7bc0e1baf2b4c5b9ca30e426f009a1a65a003d548b15c2f8cf38d434",
|
||||
"pending_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"positive_zero_warmup_exact": true,
|
||||
"steps": 1000,
|
||||
"transport_shift_exact": true
|
||||
}
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"command": [
|
||||
"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"-B",
|
||||
"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"--bundle-dir",
|
||||
"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"--dt-ms",
|
||||
"0.1"
|
||||
],
|
||||
"handshake": {
|
||||
"array_sha256": {
|
||||
"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
|
||||
"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
|
||||
"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
|
||||
},
|
||||
"decay_f32_bits": "3f7eb923",
|
||||
"dt_ms": 0.1,
|
||||
"environment": {
|
||||
"CUBLAS_WORKSPACE_CONFIG": null,
|
||||
"cuda": "13.0",
|
||||
"cuda_matmul_allow_tf32": false,
|
||||
"cudnn": 92400,
|
||||
"cudnn_allow_tf32": true,
|
||||
"cudnn_benchmark": false,
|
||||
"cudnn_deterministic": false,
|
||||
"deterministic_algorithms": false,
|
||||
"device_capability": [
|
||||
8,
|
||||
9
|
||||
],
|
||||
"device_index": 0,
|
||||
"device_name": "NVIDIA GeForce RTX 4090",
|
||||
"float32_matmul_precision": "highest",
|
||||
"numpy": "2.2.6",
|
||||
"precision_configuration_changed_by_component": false,
|
||||
"torch": "2.14.0+cu130"
|
||||
},
|
||||
"files_sha256": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"gain_f32_bits": "3c23d70a",
|
||||
"h_units": "legacy mV",
|
||||
"initial_state": "float32 zeros",
|
||||
"n_apl": 2,
|
||||
"n_kc": 4064,
|
||||
"n_neurons": 166700,
|
||||
"ok": true,
|
||||
"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
|
||||
"protocol": "phase4ag-apl-v1"
|
||||
}
|
||||
}
|
||||
+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
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "run:\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 dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\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_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 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": "run:\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_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "run:\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()\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "run:\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\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+17
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "run:\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 dt = _array_defaultclock_dt[0]\n lastspike = _array_default_neurons_lastspike\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\n t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\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_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "run:\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_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "run:\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()\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "run:\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\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+77
@@ -0,0 +1,77 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"requests.npz": "9f87b434e8da524ce31b4563ad89ab47189dc7035c9f827840f3250aad81083d",
|
||||
"spec.json": "acb231fe2f6ee50a48bdd9abb1fd7422d7a77b6a471ad4e5793ef9a4ba9f9c4c"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fb9033398abb7585ae63472ced9d8c8cab787f69965dc9ce5fa409ab105bc1bc",
|
||||
"connectivity.parquet": "1362c1102b74f5552f4b01fca956574fd6252f749e31493c4a00a1bc9eab00b3",
|
||||
"dst.npy": "ce20f7433f78442c717ad2a7b5394cf4a3b109d990b4be0788040b7b0f4ed150",
|
||||
"edge_id.npy": "49260615ff2b6432ce3dea9ce2c7ab8f83d06c2fb7491e58e08355fcedf86a30",
|
||||
"nodes.npy": "5b511a98e8fbf8d65bbc70b93e75ecae19366f650b7842a45611bfad62ed83dc",
|
||||
"ptr.npy": "fe43420f11e6242a11f8199ae7f097cab4b8a6db181d36f2a46b9e46670acbb4",
|
||||
"signed_raw.npy": "f9884ccb8930b32420b27c69271df78034a8350fe5a5c9b76a63c259268efc09",
|
||||
"src.npy": "bfd625ce690fc21eb0a8b9dede831688ab73b789045ba83ddf4baeefeddf1fc3",
|
||||
"weights.npy": "462839ffc3feb8f6c278044488082b1c8e410e7f23229d6b5e29d1b7042416a1"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 100.0,
|
||||
"expected_external_events": 105.90211181640625,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/full",
|
||||
"kind": "calibration",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"mode": "physiological",
|
||||
"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": "cal-physiological-00-s51101",
|
||||
"odor_id": "OMSUIQOIVADKIM-RXMQYKEDSA-N",
|
||||
"odor_index": 0,
|
||||
"odor_name": "ethyl (R)-(-)-3-hydroxybutanoate",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"rate_sum_hz": 1059.0211181640625,
|
||||
"record_history": false,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"seed": 51101,
|
||||
"steps": 1000,
|
||||
"target_count": 48,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
1000,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
1000,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
1000,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
1000,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": false,
|
||||
"history_shape": [
|
||||
1000,
|
||||
166700
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
+34
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"advance_calls": 1000,
|
||||
"forwarding_contract": "sealed AE/AF design",
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"limit": "Current wrapper was not independently rerun without the queue proxy",
|
||||
"new_unwrapped_repeat_performed": false,
|
||||
"original_edge_ids_not_sorted_or_filtered": true,
|
||||
"peek_calls": 1000,
|
||||
"post_threshold_pre_synaptic_guard_observed": true,
|
||||
"same_peek_object_returned": true,
|
||||
"zero_weight_and_refractory_rejected_edges_retained": true
|
||||
}
|
||||
+122
@@ -0,0 +1,122 @@
|
||||
tick,neuron_idx,attempted,accepted,rejected
|
||||
1,522,1,1,0
|
||||
26,2010,1,1,0
|
||||
27,7679,1,1,0
|
||||
31,4311,1,1,0
|
||||
37,7781,1,1,0
|
||||
40,2601,1,1,0
|
||||
42,3345,1,1,0
|
||||
58,1011,1,1,0
|
||||
59,2493,1,1,0
|
||||
69,5519,1,1,0
|
||||
81,7679,1,1,0
|
||||
86,3252,1,1,0
|
||||
95,1514,1,1,0
|
||||
102,3252,1,0,1
|
||||
103,12644,1,1,0
|
||||
115,130497,1,1,0
|
||||
127,2970,1,1,0
|
||||
130,4505,1,0,1
|
||||
137,2010,1,1,0
|
||||
158,130753,1,1,0
|
||||
162,2029,1,1,0
|
||||
162,130472,1,1,0
|
||||
163,2010,1,1,0
|
||||
181,522,1,1,0
|
||||
190,10769,1,1,0
|
||||
195,360,1,1,0
|
||||
198,2493,1,1,0
|
||||
199,1011,1,1,0
|
||||
241,1011,1,1,0
|
||||
247,10769,1,1,0
|
||||
248,6842,1,0,1
|
||||
252,2010,1,1,0
|
||||
253,4026,1,1,0
|
||||
259,360,1,1,0
|
||||
260,4026,1,0,1
|
||||
265,999,1,1,0
|
||||
270,3345,1,1,0
|
||||
271,2231,1,1,0
|
||||
288,1514,1,0,1
|
||||
299,3161,1,1,0
|
||||
300,522,1,0,1
|
||||
319,3252,1,1,0
|
||||
326,1514,1,1,0
|
||||
331,2601,1,1,0
|
||||
335,9975,1,1,0
|
||||
345,2586,1,0,1
|
||||
365,3202,1,0,1
|
||||
377,3252,1,0,1
|
||||
412,1514,1,1,0
|
||||
413,2493,1,1,0
|
||||
417,130753,1,1,0
|
||||
418,130753,1,0,1
|
||||
425,6842,1,1,0
|
||||
431,360,1,1,0
|
||||
444,9370,1,1,0
|
||||
445,189,1,1,0
|
||||
458,9370,1,0,1
|
||||
474,331,1,1,0
|
||||
480,1011,1,1,0
|
||||
483,6842,1,1,0
|
||||
492,3202,1,1,0
|
||||
497,189,1,0,1
|
||||
500,2029,1,1,0
|
||||
501,9188,1,1,0
|
||||
503,7679,1,1,0
|
||||
510,6842,1,1,0
|
||||
528,1011,1,1,0
|
||||
530,2535,1,1,0
|
||||
588,522,1,0,1
|
||||
589,1011,1,1,0
|
||||
605,3252,1,0,1
|
||||
607,12644,1,1,0
|
||||
609,4505,1,0,1
|
||||
623,4311,1,0,1
|
||||
633,3161,1,1,0
|
||||
637,131226,1,1,0
|
||||
650,2010,1,0,1
|
||||
652,331,1,1,0
|
||||
659,3252,1,0,1
|
||||
667,8504,1,1,0
|
||||
674,14839,1,1,0
|
||||
676,360,1,0,1
|
||||
680,1011,1,1,0
|
||||
687,2347,1,0,1
|
||||
697,522,1,1,0
|
||||
704,10769,1,1,0
|
||||
704,21802,1,1,0
|
||||
709,9975,1,1,0
|
||||
722,2970,1,0,1
|
||||
724,3202,1,1,0
|
||||
743,2231,1,1,0
|
||||
749,12644,1,1,0
|
||||
752,2029,1,1,0
|
||||
752,2970,1,1,0
|
||||
757,4311,1,1,0
|
||||
764,5519,1,1,0
|
||||
778,2586,1,1,0
|
||||
785,360,1,1,0
|
||||
787,999,1,0,1
|
||||
815,522,1,1,0
|
||||
835,2010,1,1,0
|
||||
844,130753,1,0,1
|
||||
853,2231,1,1,0
|
||||
862,10769,1,0,1
|
||||
867,1514,1,1,0
|
||||
872,1191,1,1,0
|
||||
874,2029,1,0,1
|
||||
878,1927,1,1,0
|
||||
880,131226,1,1,0
|
||||
901,130472,1,0,1
|
||||
904,130497,1,0,1
|
||||
906,1514,1,1,0
|
||||
920,522,1,1,0
|
||||
942,7679,1,1,0
|
||||
951,5519,1,1,0
|
||||
952,5519,1,0,1
|
||||
955,2493,1,1,0
|
||||
961,360,1,1,0
|
||||
961,130753,1,1,0
|
||||
989,2601,1,1,0
|
||||
993,14839,1,1,0
|
||||
|
+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_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
+51
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"APL_applied_sha256": "02576a8aea786e600bc165d53a1d13853489b3e5d27ddd38c28e1a6d888b85de",
|
||||
"APL_fast_edges_zeroed": 5845,
|
||||
"APL_finite_sequence_partition_exact": true,
|
||||
"APL_pending_tail_sha256": "830e2f33958a35666f61a219c72025b06970f1043971310f56f112253c6c816d",
|
||||
"APL_precision": "float32",
|
||||
"APL_timing_audit_sha256": "5106f691b5f29d2264eb10327a9cb690faaa2f5ab9863d021ca3a23e2b3d1e22",
|
||||
"APL_transport_shift_exact": true,
|
||||
"APL_worker_exit_status": 0,
|
||||
"APL_worker_ticks": 1000,
|
||||
"apl_delay_steps": 18,
|
||||
"apl_fast_edges_zeroed": 5845,
|
||||
"applicable_requests": 93,
|
||||
"backend": "shared-APL-component closed-loop Brian2 oracle",
|
||||
"candidate_trace_used_as_input": false,
|
||||
"complete": true,
|
||||
"completed_ticks": 1000,
|
||||
"edges": 25582938,
|
||||
"effective_weights_raw_sha256": "b8b8f43048e0ef03943e607666319d25363bed3b2564fdc02b4149837e7eedc9",
|
||||
"events_sha256": "c3fae1b29cc5cecc42dae1df26f27e916031a96f17fe7352d7d04d8efc84fce6",
|
||||
"future_generated_decay_tail_included": false,
|
||||
"history_recorded": false,
|
||||
"independent_CPU_all_components_claim": false,
|
||||
"initial_zero_prefix": 0,
|
||||
"kc_targets_present": 4064,
|
||||
"name": "cal-physiological-00-s51101",
|
||||
"no_poi": true,
|
||||
"no_training": true,
|
||||
"nodes": 166700,
|
||||
"output_budget_bytes": 25769803776,
|
||||
"output_bytes_before_summary": 1759755793,
|
||||
"pass_all": true,
|
||||
"pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate",
|
||||
"pending_generated_count": 18,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_tail_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"precision": "float64",
|
||||
"queue_sha256": "60ed68d5b5f038836eee21462e0f27c96b452cb1f72671f051784378be615830",
|
||||
"ref_rejected_requests": 28,
|
||||
"requests": 121,
|
||||
"scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation",
|
||||
"single_continuous_run": true,
|
||||
"source_and_inputs_unchanged": true,
|
||||
"spikes": 29672,
|
||||
"state_hashes_sha256": "d66cbd41b70337274e5c19721ebbf673bd8b68aef931bde9c16ac4d056d631a2",
|
||||
"steps": 1000,
|
||||
"weights_sha256": "ac433da4b0ac3cdc20c526d99a7158c3dc2298b62c10784f9dcd7db1f089214b"
|
||||
}
|
||||
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"KC_targets_in_case": 20,
|
||||
"base_graph_files_modified": false,
|
||||
"base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585",
|
||||
"count": 50,
|
||||
"effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406",
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"fullgraph_required_mask_count": 5845,
|
||||
"graded_targets": "only KC identities present in this LIF case",
|
||||
"ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825",
|
||||
"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"APL_generation_steps_after_window": 0,
|
||||
"apl_delay_steps": 18,
|
||||
"applied_generated_rows": 382,
|
||||
"brain_steps_after_window": 0,
|
||||
"brain_tail_advanced": false,
|
||||
"closed_loop_branch_integrals_claimed_equal": false,
|
||||
"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
|
||||
"final_apl_state_float32": [
|
||||
21.19933319091797,
|
||||
16.401752471923828
|
||||
],
|
||||
"final_apl_state_nonzero": true,
|
||||
"final_apl_state_u32_bits": [
|
||||
1101633596,
|
||||
1099118282
|
||||
],
|
||||
"finite_generated_sequence_partition_exact": true,
|
||||
"future_apl_decay_or_feedback_generated": false,
|
||||
"future_generated_decay_tail_included": false,
|
||||
"generated_rows": 400,
|
||||
"initial_zero_prefix": 0,
|
||||
"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
|
||||
"pending_application_tick_start": 400,
|
||||
"pending_application_tick_stop_exclusive": 418,
|
||||
"pending_file_sha256": "c3d9819ada9bc203974cdf8ed98a7cbea387894da48c7287e268ae0d90cc7125",
|
||||
"pending_generated_count": 18,
|
||||
"pending_generated_tick_start": 382,
|
||||
"pending_generated_tick_stop_exclusive": 400,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_raw_sha256": "8e42761721e6457d8c7917d69267cd5919182137bfaf9cfb1e8933e4b41239ce",
|
||||
"pending_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"positive_zero_warmup_exact": true,
|
||||
"steps": 400,
|
||||
"transport_shift_exact": true
|
||||
}
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"command": [
|
||||
"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"-B",
|
||||
"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"--bundle-dir",
|
||||
"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"--dt-ms",
|
||||
"0.1"
|
||||
],
|
||||
"handshake": {
|
||||
"array_sha256": {
|
||||
"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
|
||||
"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
|
||||
"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
|
||||
},
|
||||
"decay_f32_bits": "3f7eb923",
|
||||
"dt_ms": 0.1,
|
||||
"environment": {
|
||||
"CUBLAS_WORKSPACE_CONFIG": null,
|
||||
"cuda": "13.0",
|
||||
"cuda_matmul_allow_tf32": false,
|
||||
"cudnn": 92400,
|
||||
"cudnn_allow_tf32": true,
|
||||
"cudnn_benchmark": false,
|
||||
"cudnn_deterministic": false,
|
||||
"deterministic_algorithms": false,
|
||||
"device_capability": [
|
||||
8,
|
||||
9
|
||||
],
|
||||
"device_index": 0,
|
||||
"device_name": "NVIDIA GeForce RTX 4090",
|
||||
"float32_matmul_precision": "highest",
|
||||
"numpy": "2.2.6",
|
||||
"precision_configuration_changed_by_component": false,
|
||||
"torch": "2.14.0+cu130"
|
||||
},
|
||||
"files_sha256": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"gain_f32_bits": "3c23d70a",
|
||||
"h_units": "legacy mV",
|
||||
"initial_state": "float32 zeros",
|
||||
"n_apl": 2,
|
||||
"n_kc": 4064,
|
||||
"n_neurons": 166700,
|
||||
"ok": true,
|
||||
"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
|
||||
"protocol": "phase4ag-apl-v1"
|
||||
}
|
||||
}
|
||||
@@ -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
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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 lastspike = _array_default_neurons_lastspike\n dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\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_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\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 _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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 lastspike = _array_default_neurons_lastspike\n dt = _array_defaultclock_dt[0]\n v = _array_default_neurons_v.copy()\n t = _array_defaultclock_t[0]\n g = _array_default_neurons_g.copy()\n not_refractory = _array_default_neurons_not_refractory.copy()\n rfc = _array_default_neurons_rfc\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_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\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": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\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 _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_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": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "before_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nafter_run:\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"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"arrays.npz": "61d667618406364f38d34a9eb2af328a74c662b17b63dc9ac33f90ab06952df7",
|
||||
"requests.npz": "817f0fb0919c87927068fb4923854e2d2bfd1f362a6401695a26127ac032e019",
|
||||
"spec.json": "182348271356340a6c0e9982dc5104b349ffc02e703384a6cca6603f59faed40"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f",
|
||||
"connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b",
|
||||
"dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6",
|
||||
"edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765",
|
||||
"nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd",
|
||||
"ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81",
|
||||
"signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471",
|
||||
"src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f",
|
||||
"weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 40.0,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures",
|
||||
"kind": "fixture",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"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": "high_fanin",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"record_history": true,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"steps": 400,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
400,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
400,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
400,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
400,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": true,
|
||||
"history_shape": [
|
||||
400,
|
||||
44
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"advance_calls": 400,
|
||||
"forwarding_contract": "sealed AE/AF design",
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"limit": "Current wrapper was not independently rerun without the queue proxy",
|
||||
"new_unwrapped_repeat_performed": false,
|
||||
"original_edge_ids_not_sorted_or_filtered": true,
|
||||
"peek_calls": 400,
|
||||
"post_threshold_pre_synaptic_guard_observed": true,
|
||||
"same_peek_object_returned": true,
|
||||
"zero_weight_and_refractory_rejected_edges_retained": true
|
||||
}
|
||||
@@ -0,0 +1,115 @@
|
||||
tick,neuron_idx,attempted,accepted,rejected
|
||||
0,30,1,1,0
|
||||
0,71,1,1,0
|
||||
0,112,1,1,0
|
||||
0,120,1,1,0
|
||||
0,189,1,1,0
|
||||
0,296,1,1,0
|
||||
0,434,1,1,0
|
||||
0,484,1,1,0
|
||||
0,522,1,1,0
|
||||
0,530,1,1,0
|
||||
0,728,1,1,0
|
||||
0,813,1,1,0
|
||||
0,1590,1,1,0
|
||||
0,2010,1,1,0
|
||||
0,2160,1,1,0
|
||||
0,3252,1,1,0
|
||||
0,5693,1,1,0
|
||||
0,7784,1,1,0
|
||||
0,10679,1,1,0
|
||||
0,13533,1,1,0
|
||||
0,20721,1,1,0
|
||||
0,23644,1,1,0
|
||||
0,23943,1,1,0
|
||||
0,27845,1,1,0
|
||||
0,30590,1,1,0
|
||||
0,33250,1,1,0
|
||||
0,33327,1,1,0
|
||||
0,38891,1,1,0
|
||||
0,57446,1,1,0
|
||||
0,58405,1,1,0
|
||||
0,82235,1,1,0
|
||||
0,82312,1,1,0
|
||||
0,88824,1,1,0
|
||||
0,100135,1,1,0
|
||||
0,107868,1,1,0
|
||||
0,120835,1,1,0
|
||||
0,130612,1,1,0
|
||||
0,133666,1,1,0
|
||||
22,30,1,1,0
|
||||
22,71,1,1,0
|
||||
22,112,1,1,0
|
||||
22,120,1,1,0
|
||||
22,189,1,1,0
|
||||
22,296,1,1,0
|
||||
22,434,1,1,0
|
||||
22,484,1,1,0
|
||||
22,522,1,1,0
|
||||
22,530,1,1,0
|
||||
22,728,1,1,0
|
||||
22,813,1,1,0
|
||||
22,1590,1,1,0
|
||||
22,2010,1,1,0
|
||||
22,2160,1,1,0
|
||||
22,3252,1,1,0
|
||||
22,5693,1,1,0
|
||||
22,7784,1,1,0
|
||||
22,10679,1,1,0
|
||||
22,13533,1,1,0
|
||||
22,20721,1,1,0
|
||||
22,23644,1,1,0
|
||||
22,23943,1,1,0
|
||||
22,27845,1,1,0
|
||||
22,30590,1,1,0
|
||||
22,33250,1,1,0
|
||||
22,33327,1,1,0
|
||||
22,38891,1,1,0
|
||||
22,57446,1,1,0
|
||||
22,58405,1,1,0
|
||||
22,82235,1,1,0
|
||||
22,82312,1,1,0
|
||||
22,88824,1,1,0
|
||||
22,100135,1,1,0
|
||||
22,107868,1,1,0
|
||||
22,120835,1,1,0
|
||||
22,130612,1,1,0
|
||||
22,133666,1,1,0
|
||||
44,30,1,1,0
|
||||
44,71,1,1,0
|
||||
44,112,1,1,0
|
||||
44,120,1,1,0
|
||||
44,189,1,1,0
|
||||
44,296,1,1,0
|
||||
44,434,1,1,0
|
||||
44,484,1,1,0
|
||||
44,522,1,1,0
|
||||
44,530,1,1,0
|
||||
44,728,1,1,0
|
||||
44,813,1,1,0
|
||||
44,1590,1,1,0
|
||||
44,2010,1,1,0
|
||||
44,2160,1,1,0
|
||||
44,3252,1,1,0
|
||||
44,5693,1,1,0
|
||||
44,7784,1,1,0
|
||||
44,10679,1,1,0
|
||||
44,13533,1,1,0
|
||||
44,20721,1,1,0
|
||||
44,23644,1,1,0
|
||||
44,23943,1,1,0
|
||||
44,27845,1,1,0
|
||||
44,30590,1,1,0
|
||||
44,33250,1,1,0
|
||||
44,33327,1,1,0
|
||||
44,38891,1,1,0
|
||||
44,57446,1,1,0
|
||||
44,58405,1,1,0
|
||||
44,82235,1,1,0
|
||||
44,82312,1,1,0
|
||||
44,88824,1,1,0
|
||||
44,100135,1,1,0
|
||||
44,107868,1,1,0
|
||||
44,120835,1,1,0
|
||||
44,130612,1,1,0
|
||||
44,133666,1,1,0
|
||||
|
@@ -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_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"APL_applied_sha256": "f736e880d7e5a0686275e1b2be8d2c65268998df9c7e3e63862157cde395ace5",
|
||||
"APL_fast_edges_zeroed": 50,
|
||||
"APL_finite_sequence_partition_exact": true,
|
||||
"APL_pending_tail_sha256": "c3d9819ada9bc203974cdf8ed98a7cbea387894da48c7287e268ae0d90cc7125",
|
||||
"APL_precision": "float32",
|
||||
"APL_timing_audit_sha256": "4d4ca8e0260b7f7fe271230090007026de7b5b0c4697e7671a85bd2c9fa6e67c",
|
||||
"APL_transport_shift_exact": true,
|
||||
"APL_worker_exit_status": 0,
|
||||
"APL_worker_ticks": 400,
|
||||
"apl_delay_steps": 18,
|
||||
"apl_fast_edges_zeroed": 50,
|
||||
"applicable_requests": 114,
|
||||
"backend": "shared-APL-component closed-loop Brian2 oracle",
|
||||
"candidate_trace_used_as_input": false,
|
||||
"complete": true,
|
||||
"completed_ticks": 400,
|
||||
"edges": 287,
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"events_sha256": "86cdf5a06b77c8ff57cd82cb8a814fd3ac0130ef55bdc03fe74dacbc4bc01f95",
|
||||
"future_generated_decay_tail_included": false,
|
||||
"history_recorded": true,
|
||||
"independent_CPU_all_components_claim": false,
|
||||
"initial_zero_prefix": 0,
|
||||
"kc_targets_present": 20,
|
||||
"name": "high_fanin",
|
||||
"no_poi": true,
|
||||
"no_training": true,
|
||||
"nodes": 44,
|
||||
"output_budget_bytes": 25769803776,
|
||||
"output_bytes_before_summary": 931190433,
|
||||
"pass_all": true,
|
||||
"pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate",
|
||||
"pending_generated_count": 18,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_tail_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"precision": "float64",
|
||||
"queue_sha256": "02252a8546c104e0808ab55ca181e8e32c181ae9ad0ccbe13c33f008474725f6",
|
||||
"ref_rejected_requests": 0,
|
||||
"requests": 114,
|
||||
"scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation",
|
||||
"single_continuous_run": true,
|
||||
"source_and_inputs_unchanged": true,
|
||||
"spikes": 157,
|
||||
"state_hashes_sha256": "57b02271a785b9ed8cfb94aa1e286443a71164e1b1f4a27039085b15d5cb90c8",
|
||||
"steps": 400,
|
||||
"weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406"
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"KC_targets_in_case": 20,
|
||||
"base_graph_files_modified": false,
|
||||
"base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585",
|
||||
"count": 50,
|
||||
"effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406",
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"fullgraph_required_mask_count": 5845,
|
||||
"graded_targets": "only KC identities present in this LIF case",
|
||||
"ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825",
|
||||
"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
|
||||
}
|
||||
+39
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"APL_generation_steps_after_window": 0,
|
||||
"apl_delay_steps": 18,
|
||||
"applied_generated_rows": 82,
|
||||
"brain_steps_after_window": 0,
|
||||
"brain_tail_advanced": false,
|
||||
"closed_loop_branch_integrals_claimed_equal": false,
|
||||
"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
|
||||
"final_apl_state_float32": [
|
||||
8.997597694396973,
|
||||
0.0
|
||||
],
|
||||
"final_apl_state_nonzero": true,
|
||||
"final_apl_state_u32_bits": [
|
||||
1091565097,
|
||||
0
|
||||
],
|
||||
"finite_generated_sequence_partition_exact": true,
|
||||
"future_apl_decay_or_feedback_generated": false,
|
||||
"future_generated_decay_tail_included": false,
|
||||
"generated_rows": 100,
|
||||
"initial_zero_prefix": 0,
|
||||
"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
|
||||
"pending_application_tick_start": 100,
|
||||
"pending_application_tick_stop_exclusive": 118,
|
||||
"pending_file_sha256": "bad1287e543dacacbc0150fda8c9a40d8d0739a8bd58f776203775d394200019",
|
||||
"pending_generated_count": 18,
|
||||
"pending_generated_tick_start": 82,
|
||||
"pending_generated_tick_stop_exclusive": 100,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_raw_sha256": "5ecef0c2af43cad7184f9934781a20d5da400b9fa7ca42dbd8b3e97740af2206",
|
||||
"pending_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"positive_zero_warmup_exact": true,
|
||||
"steps": 100,
|
||||
"transport_shift_exact": true
|
||||
}
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"command": [
|
||||
"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"-B",
|
||||
"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"--bundle-dir",
|
||||
"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"--dt-ms",
|
||||
"0.1"
|
||||
],
|
||||
"handshake": {
|
||||
"array_sha256": {
|
||||
"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
|
||||
"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
|
||||
"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
|
||||
},
|
||||
"decay_f32_bits": "3f7eb923",
|
||||
"dt_ms": 0.1,
|
||||
"environment": {
|
||||
"CUBLAS_WORKSPACE_CONFIG": null,
|
||||
"cuda": "13.0",
|
||||
"cuda_matmul_allow_tf32": false,
|
||||
"cudnn": 92400,
|
||||
"cudnn_allow_tf32": true,
|
||||
"cudnn_benchmark": false,
|
||||
"cudnn_deterministic": false,
|
||||
"deterministic_algorithms": false,
|
||||
"device_capability": [
|
||||
8,
|
||||
9
|
||||
],
|
||||
"device_index": 0,
|
||||
"device_name": "NVIDIA GeForce RTX 4090",
|
||||
"float32_matmul_precision": "highest",
|
||||
"numpy": "2.2.6",
|
||||
"precision_configuration_changed_by_component": false,
|
||||
"torch": "2.14.0+cu130"
|
||||
},
|
||||
"files_sha256": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"gain_f32_bits": "3c23d70a",
|
||||
"h_units": "legacy mV",
|
||||
"initial_state": "float32 zeros",
|
||||
"n_apl": 2,
|
||||
"n_kc": 4064,
|
||||
"n_neurons": 166700,
|
||||
"ok": true,
|
||||
"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
|
||||
"protocol": "phase4ag-apl-v1"
|
||||
}
|
||||
}
|
||||
+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
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "run:\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 t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 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": "run:\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\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "spikemonitor_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "run:\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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": "run:\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 t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\n g = _array_default_neurons_g.copy()\n lastspike = _array_default_neurons_lastspike\n rfc = _array_default_neurons_rfc\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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\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": "run:\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "run:\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\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "spikemonitor_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_push_spikes"
|
||||
},
|
||||
{
|
||||
"generated_code": "run:\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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": "run:\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\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\nafter_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"arrays.npz": "c256234eec74752513c95d961233e7c1a7b5caa20deaa9b4913be9f539b2f1d8",
|
||||
"requests.npz": "38d5b1a50b3c05c23f5652847e3598b1e7c9f9ad217442de0617054727b8c595",
|
||||
"spec.json": "e84be68bc75f5683ef3af3fd838c7d1875d63f2f77bce59821bff7f09c1e2725"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f",
|
||||
"connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b",
|
||||
"dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6",
|
||||
"edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765",
|
||||
"nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd",
|
||||
"ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81",
|
||||
"signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471",
|
||||
"src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f",
|
||||
"weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 10.0,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures",
|
||||
"kind": "fixture",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"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": "kc_ref_boundary",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"record_history": true,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"steps": 100,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
100,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
100,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
100,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
100,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": true,
|
||||
"history_shape": [
|
||||
100,
|
||||
44
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
+34
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"advance_calls": 100,
|
||||
"forwarding_contract": "sealed AE/AF design",
|
||||
"installations": [
|
||||
"default_synapses_pre_codeobject",
|
||||
"default_synapses_pre_codeobject_1"
|
||||
],
|
||||
"limit": "Current wrapper was not independently rerun without the queue proxy",
|
||||
"new_unwrapped_repeat_performed": false,
|
||||
"original_edge_ids_not_sorted_or_filtered": true,
|
||||
"peek_calls": 100,
|
||||
"post_threshold_pre_synaptic_guard_observed": true,
|
||||
"same_peek_object_returned": true,
|
||||
"zero_weight_and_refractory_rejected_edges_retained": true
|
||||
}
|
||||
@@ -0,0 +1,7 @@
|
||||
tick,neuron_idx,attempted,accepted,rejected
|
||||
0,7784,1,1,0
|
||||
1,7784,1,0,1
|
||||
21,7784,1,0,1
|
||||
22,7784,1,1,0
|
||||
23,7784,1,0,1
|
||||
44,7784,1,1,0
|
||||
|
@@ -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_legacy_apl_unconditional (NetworkOperation) | -- | 100. us (every step) | end | 0 | yes \noracle_after_apl_record (NetworkOperation) | -- | 100. us (every step) | end | 1 | yes \nzz_apl_queue_installer (QueueInstaller) | -- | 100. us (every step) | end | 1000000 | yes "
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"APL_applied_sha256": "103d34563197b339b98bcbc08c60829f0eea0a580811cb50cba9c783a3a37897",
|
||||
"APL_fast_edges_zeroed": 50,
|
||||
"APL_finite_sequence_partition_exact": true,
|
||||
"APL_pending_tail_sha256": "bad1287e543dacacbc0150fda8c9a40d8d0739a8bd58f776203775d394200019",
|
||||
"APL_precision": "float32",
|
||||
"APL_timing_audit_sha256": "6ebb31410900e5195b2e00f947e912f37078ef84731003c863b7e0df60750a54",
|
||||
"APL_transport_shift_exact": true,
|
||||
"APL_worker_exit_status": 0,
|
||||
"APL_worker_ticks": 100,
|
||||
"apl_delay_steps": 18,
|
||||
"apl_fast_edges_zeroed": 50,
|
||||
"applicable_requests": 3,
|
||||
"backend": "shared-APL-component closed-loop Brian2 oracle",
|
||||
"candidate_trace_used_as_input": false,
|
||||
"complete": true,
|
||||
"completed_ticks": 100,
|
||||
"edges": 287,
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"events_sha256": "6e702482c91247c306339d0ad9822550c8e560a8db1b50d91db775c7500701f7",
|
||||
"future_generated_decay_tail_included": false,
|
||||
"history_recorded": true,
|
||||
"independent_CPU_all_components_claim": false,
|
||||
"initial_zero_prefix": 0,
|
||||
"kc_targets_present": 20,
|
||||
"name": "kc_ref_boundary",
|
||||
"no_poi": true,
|
||||
"no_training": true,
|
||||
"nodes": 44,
|
||||
"output_budget_bytes": 25769803776,
|
||||
"output_bytes_before_summary": 781582955,
|
||||
"pass_all": true,
|
||||
"pass_all_scope": "Oracle construction, protocol and internal self-checks only; candidate comparison is a separate gate",
|
||||
"pending_generated_count": 18,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_tail_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"precision": "float64",
|
||||
"queue_sha256": "72f55a0b9cc9bec5d5e066240b81ad6a44f85b97f0f6681f22ee5d41d459ed55",
|
||||
"ref_rejected_requests": 3,
|
||||
"requests": 6,
|
||||
"scope": "Only this fixed trajectory; legacy Torch APL is a shared numerical component, not an independent CPU APL implementation",
|
||||
"single_continuous_run": true,
|
||||
"source_and_inputs_unchanged": true,
|
||||
"spikes": 4,
|
||||
"state_hashes_sha256": "9bc3c890450af2e72e6a92104c48786ee589ee56b92db49587cfdaf366b8f369",
|
||||
"steps": 100,
|
||||
"weights_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406"
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"KC_targets_in_case": 20,
|
||||
"base_graph_files_modified": false,
|
||||
"base_weight_raw_bytes_sha256": "41992774ca397eadc8b51d199c8ca6950567f363c5eebdeb7542f3a6ec8f2585",
|
||||
"count": 50,
|
||||
"effective_weights_file_sha256": "815a82e033b1f70f42595c15ad36677692521bde0befc558c34cfab624b38406",
|
||||
"effective_weights_raw_sha256": "4f55a48a0b1e07e26b5fa132bb6a9ed87194f254ab832b4c7e9657b52cd375c6",
|
||||
"fullgraph_required_mask_count": 5845,
|
||||
"graded_targets": "only KC identities present in this LIF case",
|
||||
"ordered_original_edge_id_sha256": "24009dd051cb88d5383254c72b2c6b15d7c8cb441cacb756804ddde6fac4c825",
|
||||
"rule": "zero +0 all existing fast edges whose source GLOBAL neuron is in apl_globals; no graph edge removal"
|
||||
}
|
||||
+39
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"APL_generation_steps_after_window": 0,
|
||||
"apl_delay_steps": 18,
|
||||
"applied_generated_rows": 82,
|
||||
"brain_steps_after_window": 0,
|
||||
"brain_tail_advanced": false,
|
||||
"closed_loop_branch_integrals_claimed_equal": false,
|
||||
"delay_implementation": "independent list FIFO; no candidate or timing_queue imports",
|
||||
"final_apl_state_float32": [
|
||||
2.6759791374206543,
|
||||
0.0
|
||||
],
|
||||
"final_apl_state_nonzero": true,
|
||||
"final_apl_state_u32_bits": [
|
||||
1076577086,
|
||||
0
|
||||
],
|
||||
"finite_generated_sequence_partition_exact": true,
|
||||
"future_apl_decay_or_feedback_generated": false,
|
||||
"future_generated_decay_tail_included": false,
|
||||
"generated_rows": 100,
|
||||
"initial_zero_prefix": 0,
|
||||
"meaning": "Tail contains only already generated h and any remaining initial-zero padding; never a complete future closed-loop or decay tail",
|
||||
"pending_application_tick_start": 100,
|
||||
"pending_application_tick_stop_exclusive": 118,
|
||||
"pending_file_sha256": "db76d3dea63c8e8512fbecc50b85035dfaf8df0f7e39e0127ceb3edc171f29c2",
|
||||
"pending_generated_count": 18,
|
||||
"pending_generated_tick_start": 82,
|
||||
"pending_generated_tick_stop_exclusive": 100,
|
||||
"pending_initial_zero_prefix": 0,
|
||||
"pending_raw_sha256": "0018154b9986d41042f595c8edfc7d8ea11c826130a55ddeb3eca006a946e668",
|
||||
"pending_shape": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"positive_zero_warmup_exact": true,
|
||||
"steps": 100,
|
||||
"transport_shift_exact": true
|
||||
}
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
{
|
||||
"command": [
|
||||
"/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"-B",
|
||||
"/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"--bundle-dir",
|
||||
"/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"--dt-ms",
|
||||
"0.1"
|
||||
],
|
||||
"handshake": {
|
||||
"array_sha256": {
|
||||
"apl_globals.npy": "979ef88dff30d41168ce9b377a006ccb3cd034b2dce86d54923ca0f4b87cb6a6",
|
||||
"input_matrix.npy": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"kc_globals.npy": "9e990d10a63b0f74cd7bbbdc5369082f24d5246487af944ba289bc6bf48d4b75",
|
||||
"output_matrix.npy": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9"
|
||||
},
|
||||
"decay_f32_bits": "3f7eb923",
|
||||
"dt_ms": 0.1,
|
||||
"environment": {
|
||||
"CUBLAS_WORKSPACE_CONFIG": null,
|
||||
"cuda": "13.0",
|
||||
"cuda_matmul_allow_tf32": false,
|
||||
"cudnn": 92400,
|
||||
"cudnn_allow_tf32": true,
|
||||
"cudnn_benchmark": false,
|
||||
"cudnn_deterministic": false,
|
||||
"deterministic_algorithms": false,
|
||||
"device_capability": [
|
||||
8,
|
||||
9
|
||||
],
|
||||
"device_index": 0,
|
||||
"device_name": "NVIDIA GeForce RTX 4090",
|
||||
"float32_matmul_precision": "highest",
|
||||
"numpy": "2.2.6",
|
||||
"precision_configuration_changed_by_component": false,
|
||||
"torch": "2.14.0+cu130"
|
||||
},
|
||||
"files_sha256": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"gain_f32_bits": "3c23d70a",
|
||||
"h_units": "legacy mV",
|
||||
"initial_state": "float32 zeros",
|
||||
"n_apl": 2,
|
||||
"n_kc": 4064,
|
||||
"n_neurons": 166700,
|
||||
"ok": true,
|
||||
"operator_sequence": "state.mul_(decay32); drive=mv(I32,spikes32); state.add_(drive); h=matmul(state,O32)*gain32",
|
||||
"protocol": "phase4ag-apl-v1"
|
||||
}
|
||||
}
|
||||
+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
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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\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 dt = _array_defaultclock_dt[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\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\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\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\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\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_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\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\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\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\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\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\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\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\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\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\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"APL_precision": "float32 in separate science-Python process",
|
||||
"Torch_imported_into_Brian": false,
|
||||
"author_precision": "float64",
|
||||
"codegen": "numpy",
|
||||
"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]"
|
||||
}
|
||||
+124
@@ -0,0 +1,124 @@
|
||||
[
|
||||
{
|
||||
"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\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 dt = _array_defaultclock_dt[0]\n not_refractory = _array_default_neurons_not_refractory.copy()\n t = _array_defaultclock_t[0]\n v = _array_default_neurons_v.copy()\n rfc = _array_default_neurons_rfc\n lastspike = _array_default_neurons_lastspike\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_v[:] = v\n _array_default_neurons_g[:] = g\n _array_default_neurons_not_refractory[:] = not_refractory\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\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\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 v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\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]\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\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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\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_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _array_spikemonitor_count[_events - _source_start] += 1\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\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\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\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\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 w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\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\n",
|
||||
"name": "default_synapses_pre_codeobject_1"
|
||||
}
|
||||
],
|
||||
"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\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_v[_idx] = v\n _array_default_neurons_g[_idx] = g\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\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_1"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_legacy_apl_unconditional",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_after_apl_record",
|
||||
"order": 1,
|
||||
"type": "NetworkOperation",
|
||||
"when": "end"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "zz_apl_queue_installer",
|
||||
"order": 1000000,
|
||||
"type": "QueueInstaller",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"ae_sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
|
||||
"af_sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
|
||||
"apl_delay_steps": 18,
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"bundle_hashes": {
|
||||
"apl_globals.npy": "8388c8795d49cfe5b14fda54f76ef3a35030c62ba87bd8ca6334a2dcdf186f19",
|
||||
"input_matrix.npy": "c4d3df703ea5f1d7402b8255cfa19d9744139ad1048827c23b151d8ba766fec6",
|
||||
"kc_globals.npy": "97a570aafa089f93e642b2bb07e3a3322dabb14c3c7dd431f0c8d2b3b7951fdd",
|
||||
"output_matrix.npy": "e2d0a1ccd740e6777245ebf2efcd88bf5e1da9e47308c63ec8e83d451e134a73"
|
||||
},
|
||||
"candidate_trace_used_as_input": false,
|
||||
"case_hashes": {
|
||||
"arrays.npz": "1a4ba4d3e86b06300c645f6b02cba2d8927b8ef22003f91ccfaea39b5dc24b73",
|
||||
"requests.npz": "ccaa5da628e1659f6b42a3400927cff598017d104485ca60010b45248a37eba3",
|
||||
"spec.json": "e418a1faaeacf76104a5ac7ea431dbaeca72c125da16f18a824e7871f0081c44"
|
||||
},
|
||||
"coefficients_sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64",
|
||||
"copied_oracle_sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
|
||||
"copied_oracle_source": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/oracle_apl.py",
|
||||
"graph_hashes": {
|
||||
"completeness.csv": "fe19419a07b379537e0f94176c6e60058d42f07d576c87e7bebf1c7a568a500f",
|
||||
"connectivity.parquet": "5ee85c2b1befe4cde2ca5ad3ace142bb4e338a143d99d756d348d6aeaf44db9b",
|
||||
"dst.npy": "6f50ad492013b3d14fe45c46210a68c37fb138337ae7f91eed0730f92d5cc7d6",
|
||||
"edge_id.npy": "8b3ad7ca57738feb1495d75dadb7c7cd62890ab0b763bfb46ed217d4002c8765",
|
||||
"nodes.npy": "2a65c268f42ecb99ce2a30a22f0b654a0101ab1f336388bc2e96dc7057ccc0bd",
|
||||
"ptr.npy": "98f301cd19c6e01a911249cc594431369ed593b686f870c24aee86fcb41c8f81",
|
||||
"signed_raw.npy": "8af7fa8ede8c3da396d00bee2146d01cd549c065702d8e27211df73ad1a32471",
|
||||
"src.npy": "98ccabc84a7a57fa01a92f7164e5333dbe3f90bf18efbccf3cb4f40fef326a5f",
|
||||
"weights.npy": "d751ac5a580f88071e5a876882ada072aeb8e461f76e4fbca4ad41fa4b14f865"
|
||||
},
|
||||
"legacy_apl_worker_sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
|
||||
"only_scientific_change": "transport generated graded-APL h before the unchanged end write",
|
||||
"scope": "shared-APL-component closed-loop oracle, not all-components-independent CPU equivalence",
|
||||
"spec": {
|
||||
"apl": {
|
||||
"gain": 0.01,
|
||||
"normalization": 1.0,
|
||||
"tau_ms": 20.0
|
||||
},
|
||||
"apl_bundle_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/apl-bundle",
|
||||
"apl_delay_steps": 18,
|
||||
"apl_precision": "float32",
|
||||
"budget_root": "/data/digital-drosophila/experiments/phase4aj-apl-timing",
|
||||
"coefficients_path": "/data/digital-drosophila/experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 10.0,
|
||||
"graph_dir": "/data/digital-drosophila/experiments/phase4ag-author-apl-r2/graphs/fixtures",
|
||||
"kind": "fixture",
|
||||
"legacy_apl_worker": "/data/digital-drosophila/models/male-cns-lif/author-apl-lab-v2/legacy_apl_worker.py",
|
||||
"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": "natural_request",
|
||||
"output_budget_bytes": 25769803776,
|
||||
"precision": "float64",
|
||||
"record_history": true,
|
||||
"science_python": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"steps": 100,
|
||||
"training": false
|
||||
}
|
||||
}
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"APL_SI_conversion": "h.astype(float64) * float64(0.001), then separate float64 g subtraction",
|
||||
"APL_dtype": "float32",
|
||||
"APL_h_units": "mV; gain already applied in float32",
|
||||
"APL_inhibition_file_means": "generated h at the actual closed-loop Brian spike tick, not the delayed applied value",
|
||||
"APL_input": "Brian's own actual global spikes, never candidate spikes or candidate inhibition",
|
||||
"APL_pending_file": "apl-pending-tail.float32.npy",
|
||||
"APL_pending_generated_count": 18,
|
||||
"APL_pending_initial_zero_prefix": 0,
|
||||
"APL_pending_scope": "next D scheduled applications from recorded h plus unconsumed initial zero slots; not future generated decay",
|
||||
"APL_shapes": {
|
||||
"applied": [
|
||||
100,
|
||||
4064
|
||||
],
|
||||
"drive": [
|
||||
100,
|
||||
2
|
||||
],
|
||||
"inhibition": [
|
||||
100,
|
||||
4064
|
||||
],
|
||||
"pending_tail": [
|
||||
18,
|
||||
4064
|
||||
],
|
||||
"state": [
|
||||
100,
|
||||
2
|
||||
]
|
||||
},
|
||||
"APL_transport": "applied[t]=generated_h[t-D] for t>=D, positive zero otherwise; separate independent list FIFO",
|
||||
"APL_write_schedule": "end, order=0, after original reset, unconditional raw g write; recorder end/order=1",
|
||||
"apl_delay_steps": 18,
|
||||
"complete_marker": "summary.json complete=true only after worker exit and all checks",
|
||||
"history_recorded": true,
|
||||
"history_shape": [
|
||||
100,
|
||||
44
|
||||
],
|
||||
"queue_protocol": "sealed Phase4AF queue_summary; original global edge IDs, including zero-weight/ref-rejected arrivals",
|
||||
"request_columns": [
|
||||
"tick",
|
||||
"neuron_idx",
|
||||
"attempted",
|
||||
"accepted",
|
||||
"rejected"
|
||||
],
|
||||
"state_hash_encoding": "SHA256 of contiguous little-endian raw f64 V/g and i32 ref",
|
||||
"state_hash_file": "tick-state-hashes.jsonl",
|
||||
"state_npz": [
|
||||
"step",
|
||||
"neuron_idx",
|
||||
"counts",
|
||||
"v",
|
||||
"g",
|
||||
"ref"
|
||||
]
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user