[
  {
    "active": true,
    "code_objects": [],
    "name": "default_neurons",
    "order": 0,
    "type": "NeuronGroup",
    "when": "start"
  },
  {
    "active": true,
    "code_objects": [],
    "name": "default_synapses",
    "order": 0,
    "type": "Synapses",
    "when": "start"
  },
  {
    "abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
    "active": true,
    "code_objects": [
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # scalar code\n    _vectorisation_idx = 1\n    dt = _array_defaultclock_dt[0]\n    _lio_1 = (- dt) / tau\n    _lio_2 = exp(_lio_1)\n    _lio_3 = tau / (t_mbr - tau)\n    _lio_4 = dt / t_mbr\n    _lio_5 = dt / tau\n    _lio_6 = (- dt) / t_mbr\n    _lio_7 = v_0 - v_0\n    _lio_8 = v_0 - (v_0 * exp(_lio_6))\n    _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n    _lio_10 = exp(_lio_6)\n    # vector code\n    _vectorisation_idx = LazyArange(N)\n    g = _array_default_neurons_g.copy()\n    t = _array_defaultclock_t[0]\n    dt = _array_defaultclock_dt[0]\n    rfc = _array_default_neurons_rfc\n    v = _array_default_neurons_v.copy()\n    lastspike = _array_default_neurons_lastspike\n    not_refractory = _array_default_neurons_not_refractory.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\n",
        "name": "default_neurons_stateupdater_codeobject_2",
        "type": "CallableProxyType"
      }
    ],
    "name": "default_neurons_stateupdater",
    "order": 0,
    "type": "StateUpdater",
    "user_code": "g = -g*int(not_refractory)/tau\nv = (g - v + v_0)*int(not_refractory)/t_mbr",
    "when": "groups"
  },
  {
    "active": true,
    "code_objects": [],
    "name": "oracle_seed_injection",
    "order": 0,
    "type": "NetworkOperation",
    "when": "before_thresholds"
  },
  {
    "abstract_code": "_cond = (v > v_th) and not_refractory",
    "active": true,
    "code_objects": [
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    numpy_True = _numpy.bool_(True)\n    numpy_False = _numpy.bool_(False)\n    # scalar code\n    _vectorisation_idx = 1\n    # vector code\n    _vectorisation_idx = LazyArange(N)\n    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_2",
        "type": "CallableProxyType"
      }
    ],
    "name": "default_neurons_spike_thresholder",
    "order": 0,
    "type": "Thresholder",
    "user_code": "_cond = v > v_th",
    "when": "thresholds"
  },
  {
    "abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
    "active": true,
    "code_objects": [
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    _n_events = _array_default_neurons__spikespace[-1]\n    if _n_events > 0:\n        _events = _array_default_neurons__spikespace[:_n_events]\n        # Take subgroups into account\n        if _source_start != 0 or _source_stop != _source_N:\n            _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n            _n_events = len(_events)\n        if _n_events > 0:\n            _vectorisation_idx = 1\n            _curlen = _array_spikemonitor_N[0]\n            _newlen = _curlen + _n_events\n            _owner.resize(_newlen)\n            _array_spikemonitor_N[0] = _newlen\n            _vectorisation_idx = _events\n            _idx = _events\n            _source_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\n",
        "name": "spikemonitor_codeobject",
        "type": "CallableProxyType"
      }
    ],
    "name": "spikemonitor",
    "order": 1,
    "type": "SpikeMonitor",
    "user_code": "None",
    "when": "thresholds"
  },
  {
    "abstract_code": "g += w\n",
    "active": true,
    "code_objects": [
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    _owner.initialise_queue()\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    _owner.push_spikes()\n",
        "name": "default_synapses_pre_push_spikes",
        "type": "CallableProxyType"
      },
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    _spiking_synapses = _queue.peek()\n    if len(_spiking_synapses):\n        # scalar code\n        # vector code\n        _idx = _spiking_synapses\n        _vectorisation_idx = _idx\n        #  Abstract code:  g += w\n        _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n        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_2",
        "type": "CallableProxyType"
      }
    ],
    "name": "default_synapses_pre",
    "order": -1,
    "type": "SynapticPathway",
    "user_code": "None",
    "when": "synapses"
  },
  {
    "abstract_code": "v = v_rst; w = 0; g = 0 * mV",
    "active": true,
    "code_objects": [
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # scalar code\n    _vectorisation_idx = 1\n    w = 0\n    # vector code\n    _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n    _vectorisation_idx = _idx\n    v = v_rst\n    g = 0.0\n    _array_default_neurons_g[_idx] = g\n    _array_default_neurons_v[_idx] = v\n",
        "name": "default_neurons_spike_resetter_codeobject_2",
        "type": "CallableProxyType"
      }
    ],
    "name": "default_neurons_spike_resetter",
    "order": 0,
    "type": "Resetter",
    "user_code": "None",
    "when": "resets"
  },
  {
    "abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
    "active": true,
    "code_objects": [
      {
        "generated_code": "after_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nbefore_run:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # EMPTY_CODE_BLOCK  -- overwrite in child template\nrun:\n    import numpy as _numpy\n    from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n    # Resize dynamic arrays\n    _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n    _owner.resize(_new_len)\n    # Store values\n    _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n    # scalar code\n    _vectorisation_idx = 1\n    # vector code\n    _vectorisation_idx = _array_oracle_end_state__indices\n    _idx = _array_oracle_end_state__indices\n    _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n    _source_g = _array_default_neurons_g[_idx]\n    _source_v = _array_default_neurons_v[_idx]\n    _source_lastspike = _array_default_neurons_lastspike[_idx]\n    _to_record_v = _source_v\n    _to_record_g = _source_g\n    _to_record_lastspike = _source_lastspike\n    _to_record_not_refractory = _source_not_refractory\n    _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n    _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n    _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n    _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
        "name": "oracle_end_state_codeobject",
        "type": "CallableProxyType"
      }
    ],
    "name": "oracle_end_state",
    "order": 0,
    "type": "StateMonitor",
    "user_code": "None",
    "when": "end"
  }
]