def delay_pulses_on_layer_0_and_1(net, t0s=[0., 20], i_max=50.):
#i_max = 50. #5. # (some unit)
# t0=50. # ms
#dts = 10.
w = 1. #ms
for (i,neuron) in enumerate(net.layers[0].nodes()):
neuron.i_inj = i_max*electrodes.unit_pulse(t,t0s[0],w) # the jitcode t
for neuron in net.layers[1].nodes():
neuron.i_inj = i_max*electrodes.unit_pulse(t,t0s[1],w)
def get_poisson_spike_train(rates, t0=0., time_total=100., i_max=50., w=1.):
#w = 1. #pules width ms
i_injs = []
trains = poisson_train(rates, time_total)
for train in trains:
train = train[train <= time_total]
i_inj = sum(i_max*electrodes.unit_pulse(t,t0+t_spike,w) for t_spike in train)
i_injs.append(i_inj)
return i_injs, trains
def pulse_on_layer(net, layer_idx, t0=50., i_max=50.):
w = 1. #ms
for (i,neuron) in enumerate(net.layers[layer_idx].nodes()):
neuron.i_inj = i_max*electrodes.unit_pulse(t,t0,w) # the jitcode t
def pulse_train_on_layer(net, layer_idx, t0s, i_max=50.):
w = 1. #ms
i_inj = i_max*sum(electrodes.unit_pulse(t,t0,w) for t0 in t0s)
for (i,neuron) in enumerate(net.layers[layer_idx].nodes()):
neuron.i_inj = i_inj # the jitcode t
def delay_pulses_on_presyn_0_and_1(net, t0s=[0., 20], i_max=55., w=1.0):
for (i, neuron) in enumerate(net.layers[0].nodes()):
neuron.i_inj = i_max * electrodes.unit_pulse(t, t0s[i],
w) # the jitcode t