{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "0b85a598",
"metadata": {},
"outputs": [],
"source": [
"from brian2 import *\n",
"\n",
"from adex_sine import *\n",
"\n",
"defaultclock.dt = 10 * us\n",
"\n",
"\n",
"class Model:\n",
" # Model type flags\n",
" SINE = 0\n",
" EXP2SYN = 1\n",
"\n",
" # Noise flags\n",
" HIGH = 0\n",
" LOW = 1\n",
" OFF = -1\n",
"\n",
" # Noise parameters\n",
" EXCITATORY_NOISE_VARIANCE = {HIGH: 0.5 * nS, LOW: 0.25 * nS, OFF: 0 * nS}\n",
" INHIBITORY_NOISE_VARIANCE = {HIGH: 1.25 * nS, LOW: 0.625 * nS, OFF: 0 * nS}\n",
"\n",
" # Noise mean conductance\n",
" EXCITATORY_CONDUCTANCE = 1 * nS\n",
" INHIBITORY_CONDUCTANCE = 4 * nS\n",
"\n",
" DEFAULT_PARAMETERS = {\n",
" \"sigma_flux\" : 6.75*pA, \n",
" \"c\": 85 * pF,\n",
" \"tau_w\": 19.240498 * ms,\n",
" \"b\": 0.25 * nA,\n",
" \"a\": 1.3 * nS,\n",
" \"v_T\": -45 * mV,\n",
" \"v_thresh\": 0 * mV,\n",
" \"DeltaT\": 0.2 * mV,\n",
" # EQUILIBRIUM POTENTIAL\n",
" \"e_l\": -65 * mV,\n",
" \"e_ex\": 0 * mV,\n",
" \"e_in\": -70 * mV,\n",
" # CONDUCTANCES\n",
" \"g_l\": 3 * nS,\n",
" \"mu_ex\": 0 * nS,\n",
" \"mu_in\": 0 * nS,\n",
" # EXCITATORY NOISE\n",
" \"sigma_ex\": 0 * nS,\n",
" \"tau_noise_ex\": 3 * ms,\n",
" # INHIBITORY NOISE\n",
" \"sigma_in\": 0 * nS,\n",
" \"tau_noise_in\": 10 * ms,\n",
" # SINE INPUT\n",
" \"f\": 100 * Hz,\n",
" \"A\": 0 * pA,\n",
" \"i_injected\": 0 * pA,\n",
" \"v_reset\": -70 * mV,\n",
" # m current\n",
" \"g_adapt\": 10 * nS,\n",
" \"e_k\": -90*mV,\n",
" \"beta_z\": -35*mV,\n",
" \"gamma_z\": 4*mV, #5\n",
" \"tau_z\": 100*ms,\n",
" }\n",
"\n",
" def __init__(\n",
" self, n, *, stim=None, noise=None, resistance=None, additional_vars=()\n",
" ):\n",
" if resistance is None:\n",
" raise ValueError(\"Resistance must be specified\")\n",
"\n",
" if noise is None:\n",
" raise ValueError(\"Noise must be specified\")\n",
"\n",
" self.stim_type = stim\n",
" self._input_resistance = None\n",
" self._noise_level = None\n",
" self._duration = 0\n",
" self.recorded_vars = (\"v\",) + additional_vars\n",
"\n",
" self.neurons = self.set_default(n_neuron=n)\n",
" self.set_resistance(resistance)\n",
" self.set_noise(noise)\n",
"\n",
" self.spikes = None\n",
" self.spiker = None\n",
" self.synapses = None\n",
" self.inhib_synapses = None\n",
" self.smon = None\n",
" self.network = None\n",
" self.build_network()\n",
"\n",
" def create_model(self):\n",
" return ADEX_MODEL, self.DEFAULT_PARAMETERS\n",
"\n",
" def set_default(self, n_neuron):\n",
" model, parameters = self.create_model()\n",
"\n",
" neurons = NeuronGroup(\n",
" n_neuron,\n",
" model=model,\n",
" method=\"Euler\",\n",
" name=\"neurons\",\n",
" threshold=\"v > v_thresh\",\n",
" reset=\"v = v_reset; w += b\",\n",
" )\n",
"\n",
" for parameter, value in parameters.items():\n",
" neurons.__setattr__(parameter, value)\n",
"\n",
" neurons.v = neurons.e_l # remove most of transient\n",
"\n",
" return neurons\n",
"\n",
" def set_resistance(self, level):\n",
" if level == self.LOW:\n",
" exc_conductance = self.EXCITATORY_CONDUCTANCE\n",
" inhib_conductance = self.INHIBITORY_CONDUCTANCE\n",
"\n",
" else:\n",
" exc_conductance = inhib_conductance = 0\n",
"\n",
" self._input_resistance = level\n",
" self._set_variable(\"mu_ex\", exc_conductance)\n",
" self._set_variable(\"mu_in\", inhib_conductance)\n",
"\n",
" def set_noise(self, level):\n",
" if level == self.HIGH or level == self.LOW:\n",
" exc_noise = self.EXCITATORY_NOISE_VARIANCE[level]\n",
" inhib_noise = self.INHIBITORY_NOISE_VARIANCE[level]\n",
"\n",
" else:\n",
" exc_noise = inhib_noise = 0\n",
"\n",
" self._noise_level = level\n",
" self._set_variable(\"sigma_ex\", exc_noise)\n",
" self._set_variable(\"sigma_in\", inhib_noise)\n",
"\n",
" def set_injected_current(self, amplitude):\n",
" self._set_variable(\"i_injected\", amplitude)\n",
" self._set_variable(\"A\", 0 * pA)\n",
"\n",
" def set_stimulus_current(self, amplitude):\n",
" self._set_variable(\"A\", amplitude)\n",
" self._set_variable(\"i_injected\", 0 * pA)\n",
"\n",
" @property\n",
" def f(self):\n",
" return self.neurons.f\n",
"\n",
" @f.setter\n",
" def f(self, new_f):\n",
" self._set_variable(\"f\", new_f) # this will reset smon\n",
" if self.stim_type == self.EXP2SYN:\n",
" self.spiker.T = 1 / new_f\n",
"\n",
" def run(self, duration, report=\"stdout\"):\n",
" self._duration = duration\n",
" self.network.run(duration, report=report)\n",
"\n",
" def build_network(self):\n",
" self.smon = StateMonitor(\n",
" self.neurons, self.recorded_vars, record=True, name=\"smon\"\n",
" )\n",
" self.spikes = SpikeMonitor(self.neurons, name=\"spikes\")\n",
"\n",
" self.network = Network(self.neurons, self.smon, self.spikes)\n",
"\n",
" def _set_variable(self, name, value):\n",
" self.neurons.__setattr__(name, value)\n",
" self.reset_recording()\n",
"\n",
" def reset_recording(self):\n",
" try:\n",
" self.network\n",
" except AttributeError:\n",
" return # network not yet initialized\n",
"\n",
" self.network.remove(self.smon, self.spikes)\n",
"\n",
" self.smon = StateMonitor(\n",
" self.neurons, self.recorded_vars, record=True, name=\"smon\"\n",
" )\n",
" self.spikes = SpikeMonitor(self.neurons, name=\"spikes\")\n",
"\n",
" self.network.add(self.smon, self.spikes)\n",
"\n",
" @property\n",
" def spike_train(self):\n",
" return self.spikes.spike_trains()\n",
"\n",
" @property\n",
" def firing_rate(self):\n",
" return self.spikes.count / self.duration\n",
"\n",
" @property\n",
" def duration(self):\n",
" return self._duration\n",
"\n",
" @property\n",
" def input_resistance(self):\n",
" if self._input_resistance == self.HIGH:\n",
" return \"HIGH\"\n",
" else:\n",
" return \"LOW\"\n",
"\n",
" @property\n",
" def noise_level(self):\n",
" if self._noise_level == self.HIGH:\n",
" return \"HIGH\"\n",
" elif self._noise_level == self.LOW:\n",
" return \"LOW\"\n",
" else:\n",
" return \"NO\"\n",
"\n",
" def __repr__(self):\n",
" return f\"{self.neurons.N} Neurons with {self.input_resistance} input resistance and {self.noise_level} noise\"\n",
"\n",
" def __str__(self):\n",
" return self.__repr__()\n",
"\n",
" def store(self, name):\n",
" self.network.store(name)\n",
"\n",
" def restore(self, name):\n",
" self.network.restore(name)\n",
"\n",
" @property\n",
" def v(self):\n",
" return self.smon.v\n",
"\n",
" @property\n",
" def t(self):\n",
" return self.smon.t\n",
"\n",
" @property\n",
" def injected_current(self):\n",
" return self.neurons.i_injected\n",
"\n",
" @property\n",
" def stimulus_amplitude(self):\n",
" return self.neurons.A\n",
"\n",
"\n",
"class CurrentModel(Model):\n",
" def __init__(self, **kwargs):\n",
" super().__init__(stim=self.SINE, **kwargs)\n",
"\n",
" def create_model(self):\n",
" model, parameters = super().create_model()\n",
" model += CURRENT_INPUT\n",
"\n",
" return model, parameters\n",
"\n",
"\n",
"class SineModel(CurrentModel):\n",
" def create_model(self):\n",
" model, parameters = super().create_model()\n",
" model += SINE_INPUT\n",
"\n",
" return model, parameters\n",
"\n",
"\n",
"class SawModel(CurrentModel):\n",
" def create_model(self):\n",
" model, parameters = super().create_model()\n",
" model += SAW_INPUT\n",
"\n",
" return model, parameters\n",
"\n",
"\n",
"class SynapticModel(Model):\n",
" def __init__(self, **kwargs):\n",
" super().__init__(stim=self.EXP2SYN, **kwargs)\n",
"\n",
" SYNAPTIC_PARAMETERS = {\n",
" \"tau_input_1\": 0.4 * ms,\n",
" \"tau_input_2\": 4 * ms,\n",
" \"offset_A\": 1.48793507e-11,\n",
" \"offset_B\": -2.66359562e-08,\n",
" \"offset_C\": 1.77538800e-05,\n",
" \"offset_D\": -8.05925810e-04,\n",
" \"offset_E\": -3.51463644e-02,\n",
" \"offset_switch\": 0,\n",
" }\n",
"\n",
" def create_model(self):\n",
" model, parameters = super().create_model()\n",
" model += EXP2SYN_WAVEFORM + SUMMATION_OFFSET\n",
" parameters = {**parameters, **self.SYNAPTIC_PARAMETERS}\n",
"\n",
" return model, parameters\n",
"\n",
" def build_network(self):\n",
" super().build_network()\n",
" self.spiker = NeuronGroup(\n",
" self.neurons.N,\n",
" \"\"\"T : second (constant)\n",
" lastspike : second\"\"\",\n",
" threshold=\"timestep(t-lastspike, dt)>=timestep(T, dt)\",\n",
" reset=\"lastspike=t\",\n",
" )\n",
" self.spiker.T = 1 / self.neurons.f\n",
" self.synapses = Synapses(\n",
" self.spiker, self.neurons, on_pre=\"input_aux += 1\"\n",
" ) # connect input to neurons\n",
" self.synapses.connect(\"i==j\") # one synapse goes to one neuron\n",
"\n",
" self.network.add(self.spiker, self.synapses)\n",
"\n",
"\n",
"class SynapticCurrentModel(SynapticModel):\n",
" def __init__(self, offset=True, **kwargs):\n",
" self.offset = 1 if offset else 0\n",
" super().__init__(**kwargs)\n",
"\n",
" def create_model(self):\n",
" model, parameters = super().create_model()\n",
" model += CURRENT_INPUT + SYNAPTIC_INPUT_CURRENT\n",
" parameters = {**parameters, **{\"offset_switch\": self.offset}}\n",
"\n",
" return model, parameters\n",
"\n",
"\n",
"class SynapticConductanceModel(SynapticModel):\n",
" FLAT = 0\n",
" ACTIVE = 1\n",
"\n",
" CONDUCTANCE_PARAMETERS = {\n",
" \"A\": 0 * nS, # overwrite A to be conductance\n",
" \"g_i\": 1 * nS,\n",
" }\n",
"\n",
" INHIBITION_PARAMETERS = {\n",
" \"tau_inhibition_1\": 1 * ms,\n",
" \"tau_inhibition_2\": 10 * ms,\n",
" }\n",
"\n",
" def __init__(self, offset=ACTIVE, **kwargs):\n",
" self.offset = offset\n",
" super().__init__(**kwargs)\n",
"\n",
" def create_model(self):\n",
" model, parameters = super().create_model()\n",
" if self.offset == self.FLAT:\n",
" model += CONDUCTANCE_INPUT + SYNAPTIC_CONDUCTANCE_FLAT\n",
" parameters = {\n",
" **parameters,\n",
" **self.SYNAPTIC_PARAMETERS,\n",
" **self.CONDUCTANCE_PARAMETERS,\n",
" **{\"offset_switch\": 1},\n",
" }\n",
"\n",
" elif self.offset == self.ACTIVE:\n",
" model += CONDUCTANCE_INPUT + SYNAPTIC_CONDUCTANCE_STIM\n",
" parameters = {\n",
" **parameters,\n",
" **self.SYNAPTIC_PARAMETERS,\n",
" **self.CONDUCTANCE_PARAMETERS,\n",
" **self.INHIBITION_PARAMETERS,\n",
" }\n",
"\n",
" return model, parameters\n",
"\n",
" def build_network(self):\n",
" super().build_network()\n",
" if self.offset != self.ACTIVE:\n",
" return\n",
"\n",
" self.inhib_synapses = Synapses(\n",
" self.spiker, self.neurons, on_pre=\"input_inhib_aux += 1\", delay=2 * ms\n",
" ) # connect input to neurons\n",
" self.inhib_synapses.connect(\"i==j\") # one synapse goes to one neuron\n",
"\n",
" self.network.add(self.inhib_synapses)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c82769f2",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING Cannot use Cython, a test compilation failed: Microsoft Visual C++ 14.0 or greater is required. Get it with \"Microsoft C++ Build Tools\": https://visualstudio.microsoft.com/visual-cpp-build-tools/ (DistutilsPlatformError) [brian2.codegen.runtime.cython_rt.cython_rt.failed_compile_test]\n",
"INFO Cannot use compiled code, falling back to the numpy code generation target. Note that this will likely be slower than using compiled code. Set the code generation to numpy manually to avoid this message:\n",
"prefs.codegen.target = \"numpy\" [brian2.devices.device.codegen_fallback]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting simulation at t=0. s for a duration of 12. s\n",
"0.32415 s (2%) simulated in 10s, estimated 6m 0s remaining.\n",
"0.6406 s (5%) simulated in 20s, estimated 5m 55s remaining.\n",
"0.93958 s (7%) simulated in 30s, estimated 5m 53s remaining.\n",
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"10.02168 s (83%) simulated in 5m 0s, estimated 59s remaining.\n",
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"10.81462 s (90%) simulated in 5m 20s, estimated 35s remaining.\n",
"11.21245 s (93%) simulated in 5m 30s, estimated 23s remaining.\n",
"11.61342 s (96%) simulated in 5m 40s, estimated 11s remaining.\n",
"12. s (100%) simulated in 5m 49s\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.collections.EventCollection at 0x20686cc86a0>]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#from models import Model, SynapticConductanceModel\n",
"from brian2 import *\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"model = SynapticConductanceModel(resistance=Model.LOW, # Model.LOW, Model.HIGH\n",
" noise=Model.HIGH, n=1, # Model.OFF, Model.LOW, Model,HIGH\n",
" offset=SynapticConductanceModel.ACTIVE)\n",
"\n",
"\n",
"# model = SineModel(resistance=Model.LOW, # Model.LOW, Model.HIGH\n",
"# noise=Model.OFF, n=1, # Model.OFF, Model.LOW, Model,HIGH\n",
"# )\n",
"\n",
"\n",
"model.f = 100 * Hz\n",
"model.set_stimulus_current(400 * nS) # current should be scaled by 100x for Active Offset so (500nS is actually 5nS)\n",
"model._set_variable(\"i_injected\", 65 * pA)\n",
"\n",
"\n",
"model.run(12*second)\n",
"\n",
"spike_times = [s/ms for s in model.spike_train.values()]\n",
"\n",
"plt.eventplot(spike_times)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7f22e21e",
"metadata": {},
"outputs": [],
"source": [
"#print(spike_times)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "20e3191c",
"metadata": {},
"outputs": [],
"source": [
"import scipy.io\n",
"import numpy as np\n",
"\n",
"\n",
"file_path = 'data47.mat'\n",
"scipy.io.savemat(file_path, {'data47': spike_times[0]})\n",
"\n",
"# file_path = 'data1.mat'\n",
"# scipy.io.savemat(file_path, {'data1': spike_times[1]})\n",
"\n",
"# file_path = 'data2.mat'\n",
"# scipy.io.savemat(file_path, {'data2': spike_times[2]})\n",
"\n",
"# file_path = 'data3.mat'\n",
"# scipy.io.savemat(file_path, {'data3': spike_times[3]})\n",
"\n",
"# file_path = 'data4.mat'\n",
"# scipy.io.savemat(file_path, {'data4': spike_times[4]})\n",
"\n",
"# file_path = 'data5.mat'\n",
"# scipy.io.savemat(file_path, {'data5': spike_times[5]})\n",
"\n",
"# file_path = 'data6.mat'\n",
"# scipy.io.savemat(file_path, {'data6': spike_times[6]})\n",
"\n",
"# file_path = 'data7.mat'\n",
"# scipy.io.savemat(file_path, {'data7': spike_times[7]})\n",
"\n",
"# file_path = 'data8.mat'\n",
"# scipy.io.savemat(file_path, {'data8': spike_times[8]})\n",
"\n",
"# file_path = 'data9.mat'\n",
"# scipy.io.savemat(file_path, {'data9': spike_times[9]})\n",
"\n",
"# file_path = 'data10.mat'\n",
"# scipy.io.savemat(file_path, {'data10': spike_times[10]})\n",
"\n",
"# file_path = 'data11.mat'\n",
"# scipy.io.savemat(file_path, {'data11': spike_times[11]})\n",
"\n",
"# file_path = 'data12.mat'\n",
"# scipy.io.savemat(file_path, {'data12': spike_times[12]})\n",
"\n",
"# file_path = 'data13.mat'\n",
"# scipy.io.savemat(file_path, {'data13': spike_times[13]})\n",
"\n",
"# file_path = 'data14.mat'\n",
"# scipy.io.savemat(file_path, {'data14': spike_times[14]})\n",
"\n",
"# file_path = 'data15.mat'\n",
"# scipy.io.savemat(file_path, {'data15': spike_times[15]})\n",
"\n",
"# file_path = 'data16.mat'\n",
"# scipy.io.savemat(file_path, {'data16': spike_times[16]})\n",
"\n",
"# file_path = 'data17.mat'\n",
"# scipy.io.savemat(file_path, {'data17': spike_times[17]})\n",
"\n",
"# file_path = 'data18.mat'\n",
"# scipy.io.savemat(file_path, {'data18': spike_times[18]})\n",
"\n",
"# file_path = 'data19.mat'\n",
"# scipy.io.savemat(file_path, {'data19': spike_times[19]})\n",
"\n",
"# file_path = 'data20.mat'\n",
"# scipy.io.savemat(file_path, {'data20': spike_times[20]})\n",
"\n",
"# file_path = 'data21.mat'\n",
"# scipy.io.savemat(file_path, {'data21': spike_times[21]})\n",
"\n",
"# file_path = 'data22.mat'\n",
"# scipy.io.savemat(file_path, {'data22': spike_times[22]})\n",
"\n",
"# file_path = 'data23.mat'\n",
"# scipy.io.savemat(file_path, {'data23': spike_times[23]})\n",
"\n",
"# file_path = 'data24.mat'\n",
"# scipy.io.savemat(file_path, {'data24': spike_times[24]})\n",
"\n",
"# file_path = 'data25.mat'\n",
"# scipy.io.savemat(file_path, {'data25': spike_times[25]})\n",
"\n",
"# file_path = 'data26.mat'\n",
"# scipy.io.savemat(file_path, {'data26': spike_times[26]})\n",
"\n",
"# file_path = 'data27.mat'\n",
"# scipy.io.savemat(file_path, {'data27': spike_times[27]})\n",
"\n",
"# file_path = 'data28.mat'\n",
"# scipy.io.savemat(file_path, {'data28': spike_times[28]})\n",
"\n",
"# file_path = 'data29.mat'\n",
"# scipy.io.savemat(file_path, {'data29': spike_times[29]})\n",
"\n",
"# file_path = 'data30.mat'\n",
"# scipy.io.savemat(file_path, {'data30': spike_times[30]})\n",
"\n",
"# file_path = 'data31.mat'\n",
"# scipy.io.savemat(file_path, {'data31': spike_times[31]})\n",
"\n",
"# file_path = 'data32.mat'\n",
"# scipy.io.savemat(file_path, {'data32': spike_times[32]})\n",
"\n",
"# file_path = 'data33.mat'\n",
"# scipy.io.savemat(file_path, {'data33': spike_times[33]})\n",
"\n",
"# file_path = 'data34.mat'\n",
"# scipy.io.savemat(file_path, {'data34': spike_times[34]})\n",
"\n",
"# file_path = 'data35.mat'\n",
"# scipy.io.savemat(file_path, {'data35': spike_times[35]})\n",
"\n",
"# file_path = 'data36.mat'\n",
"# scipy.io.savemat(file_path, {'data36': spike_times[36]})\n",
"\n",
"# file_path = 'data37.mat'\n",
"# scipy.io.savemat(file_path, {'data37': spike_times[37]})\n",
"\n",
"# file_path = 'data38.mat'\n",
"# scipy.io.savemat(file_path, {'data38': spike_times[38]})\n",
"\n",
"# file_path = 'data39.mat'\n",
"# scipy.io.savemat(file_path, {'data39': spike_times[39]})\n",
"\n",
"# file_path = 'data40.mat'\n",
"# scipy.io.savemat(file_path, {'data40': spike_times[40]})\n",
"\n",
"# file_path = 'data41.mat'\n",
"# scipy.io.savemat(file_path, {'data41': spike_times[41]})\n",
"\n",
"# file_path = 'data42.mat'\n",
"# scipy.io.savemat(file_path, {'data42': spike_times[42]})\n",
"\n",
"# file_path = 'data43.mat'\n",
"# scipy.io.savemat(file_path, {'data43': spike_times[43]})\n",
"\n",
"# file_path = 'data44.mat'\n",
"# scipy.io.savemat(file_path, {'data44': spike_times[44]})\n",
"\n",
"# file_path = 'data45.mat'\n",
"# scipy.io.savemat(file_path, {'data45': spike_times[45]})\n",
"\n",
"# file_path = 'data46.mat'\n",
"# scipy.io.savemat(file_path, {'data46': spike_times[46]})\n",
"\n",
"# file_path = 'data47.mat'\n",
"# scipy.io.savemat(file_path, {'data47': spike_times[47]})\n",
"\n",
"# file_path = 'data48.mat'\n",
"# scipy.io.savemat(file_path, {'data48': spike_times[48]})\n",
"\n",
"# file_path = 'data49.mat'\n",
"# scipy.io.savemat(file_path, {'data49': spike_times[49]})\n",
"\n",
"\n",
"\n"
]
},
{
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"id": "d353eff4",
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"outputs": [],
"source": []
}
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