spikeArray = {'spike_times': [[0], [1], [13], [45], [93]]} # in ms
# Presynaptic population - Input layer - Stimuli
pop_input = sim.Population(
n_neurons,
sim.SpikeSourceArray,
spikeArray,
# sim.SpikeSourcePoisson(), #(rate=1, duration=sim_time),
label='pop_input')
# Postsynaptic population
"""
Notes:
* Interesting property about this neuron model: voltage_based_synapses = True
* Initial voltage value = -70.0
"""
pop_output = sim.Population(n_neurons, sim.Izhikevich(), label='pop_output')
sim.Projection(pop_input, pop_output, sim.OneToOneConnector(),
sim.StaticSynapse(weight=20.0, delay=2))
# == Instrument the network ====================================================
# Record all to observe.
"""
Note:
Recordables of the Izhikevich neuron model are limited with voltage, spikes, and unit(mV/ms) of the population.
"""
pop_output.record("all")
# === Run the simulation =======================================================
stn_cell_params = {
'a': stn_a,
'b': stn_b,
'c': stn_c,
'd': stn_d,
'v_init': stn_v_init,
'u_init': stn_u_init,
'tau_syn_E': tau_ampa,
'tau_syn_I': tau_gabaa,
'i_offset': current_bias_stn,
'isyn_exc': E_ampa,
'isyn_inh': E_gabaa
}
''' THE FIRST CHANNEL'''
strd1_pop1 = p.Population(numCellsPerCol_STR,
p.Izhikevich(**strd1_cell_params),
label='strd1_pop1')
strd2_pop1 = p.Population(numCellsPerCol_STR,
p.Izhikevich(**strd2_cell_params),
label='strd2_pop1')
gpe_pop1 = p.Population(numCellsPerCol_GPe,
p.Izhikevich(**gpe_cell_params),
label='gpe_pop1')
snr_pop1 = p.Population(numCellsPerCol_SNR,
p.Izhikevich(**snr_cell_params),
label='snr_pop1')
stn_pop1 = p.Population(numCellsPerCol_STN,
p.Izhikevich(**stn_cell_params),
label='stn_pop1')
fsi1_pop1 = p.Population(numCellsPerCol_FSI,
p.Izhikevich(**fsi_cell_params),
timestamp = 1.0 # simulate the network with 1.0 ms time steps
sim_time = 10 # total simulation time
# === Configure the simulator ==================================================
sim.setup(timestamp)
# === Build the network ========================================================
# Izhikevich neuron model
"""
Notes:
* Interesting property about this neuron model: voltage_based_synapses = True
* Initial voltage value = -70.0
"""
pop_exc = sim.Population(n_neurons, sim.Izhikevich(), label='pop_exc')
# == Instrument the network ====================================================
# Record all to observe.
"""
Note:
Recordables of this neuron model are limited with voltage, spikes, and unit(mV/ms) of the population.
"""
pop_exc.record("all")
# === Run the simulation =======================================================
sim.run(sim_time)
# === Plot the results =========================================================
populations = list()
projections = list()
weight_to_spike = 30
delay = 1
loopConnections = list()
for i in range(0, nNeurons):
singleConnection = ((i, (i + 1) % nNeurons, weight_to_spike, delay))
loopConnections.append(singleConnection)
injectionConnection = [(0, 0)]
spikeArray = {'spike_times': [[50]]}
populations.append(
p.Population(nNeurons, p.Izhikevich(**cell_params_izk), label='pop_1'))
populations.append(
p.Population(1, p.SpikeSourceArray(**spikeArray), label='inputSpikes_1'))
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(loopConnections),
p.StaticSynapse(weight=weight_to_spike, delay=delay)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injectionConnection),
p.StaticSynapse(weight=weight_to_spike, delay=1)))
populations[0].record(['v', 'gsyn_exc', 'gsyn_inh', 'spikes'])
p.run(runtime)