def do_run(plot):
p.setup(timestep=1.0)
cell_params_lif = {
'cm': 0.25,
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
# Parameters
nNeurons = 200
weight_to_spike = 2.0
delay = 17
runtime = 5000
p.set_number_of_neurons_per_core(p.IF_curr_exp, nNeurons / 2)
# Populations
pop = p.Population(nNeurons,
p.IF_curr_exp(**cell_params_lif),
label='pop_1')
pop2 = p.Population(nNeurons,
p.IF_curr_exp(**cell_params_lif),
label='pop_2')
# create loopConnections array for first population using numpy linspaces
loopConnections = numpy.zeros((nNeurons, nNeurons))
for i in range(nNeurons):
if i != (nNeurons - 1):
loopConnections[i, i + 1] = True
else:
loopConnections[i, 0] = True
# do the same for the second population, but just for even numbered neurons
loopConnections2 = numpy.zeros((nNeurons, nNeurons))
for i in range(0, nNeurons, 2):
if i != (nNeurons - 2):
loopConnections2[i, i + 2] = True
else:
loopConnections2[i, 0] = True
# SpikeInjector
injectionConnection = numpy.zeros((1, nNeurons))
injectionConnection[0, 0] = True
spikeArray = {'spike_times': [[0]]}
inj_pop = p.Population(1,
p.SpikeSourceArray(**spikeArray),
label='inputSpikes_1')
# Projection for injector
p.Projection(inj_pop, pop, p.ArrayConnector(injectionConnection),
p.StaticSynapse(weight=weight_to_spike, delay=1))
p.Projection(inj_pop, pop2, p.ArrayConnector(injectionConnection),
p.StaticSynapse(weight=weight_to_spike, delay=1))
# Projection within populations
p.Projection(pop, pop, p.ArrayConnector(loopConnections),
p.StaticSynapse(weight=weight_to_spike, delay=delay))
p.Projection(pop2, pop2, p.ArrayConnector(loopConnections2),
p.StaticSynapse(weight=weight_to_spike, delay=delay))
pop.record(['v', 'spikes'])
pop2.record(['v', 'spikes'])
p.run(runtime)
v = pop.get_data('v')
spikes = pop.get_data('spikes')
v2 = pop2.get_data('v')
spikes2 = pop2.get_data('spikes')
if plot:
Figure(
# raster plot of the presynaptic neurons' spike times
Panel(spikes.segments[0].spiketrains,
yticks=True,
markersize=1.2,
xlim=(0, runtime),
xticks=True),
# membrane potential of the postsynaptic neurons
Panel(v.segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[pop.label],
yticks=True,
xlim=(0, runtime),
xticks=True),
Panel(spikes2.segments[0].spiketrains,
yticks=True,
markersize=1.2,
xlim=(0, runtime),
xticks=True),
# membrane potential of the postsynaptic neurons
Panel(v2.segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[pop2.label],
yticks=True,
xlim=(0, runtime),
xticks=True),
title="Testing ArrayConnector",
annotations="Simulated with {}".format(p.name()))
plt.show()
p.end()
return v, spikes, v2, spikes2
def do_larger_array(plot):
p.setup(timestep=1.0)
n_i = 64
n_e = 64
spikeArray = {'spike_times': [0]}
input_pop = p.Population(n_e,
p.SpikeSourceArray(**spikeArray),
label='inputSpikes')
excit_pop = p.Population(n_e, p.IF_curr_exp, label='excit')
inhit_pop = p.Population(n_i, p.IF_curr_exp, label='inhib')
p.Projection(input_pop,
excit_pop,
p.AllToAllConnector(),
synapse_type=p.StaticSynapse(weight=5),
receptor_type='excitatory')
ie_conn = numpy.ones((n_i, n_e))
for i in range(n_e):
ie_conn[i, i] = 0
p.Projection(excit_pop,
inhit_pop,
p.OneToOneConnector(),
synapse_type=p.StaticSynapse(weight=2),
receptor_type='inhibitory')
ie_projec = p.Projection(inhit_pop,
excit_pop,
p.ArrayConnector(ie_conn),
synapse_type=p.StaticSynapse(weight=3),
receptor_type='excitatory')
excit_pop.record(["spikes", "v"])
runtime = 1000
p.run(runtime)
ie_conns = ie_projec.get(['weight', 'delay'], 'list')
v = excit_pop.get_data("v")
spikes = excit_pop.get_data("spikes")
if plot:
Figure(
# raster plot of the presynaptic neurons' spike times
Panel(spikes.segments[0].spiketrains,
yticks=True,
markersize=1.2,
xlim=(0, runtime),
xticks=True),
# membrane potential of the postsynaptic neurons
Panel(v.segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[inhit_pop.label],
yticks=True,
xlim=(0, runtime),
xticks=True),
title="Testing ArrayConnector",
annotations="Simulated with {}".format(p.name()))
plt.show()
p.end()
return v, spikes, ie_conns