def single_neuron_time(time,
dt,
scale,
step_size,
I_function,
shape,
spike_trace,
additive_spike_trace,
tau_s,
trace_scale,
is_inhibitory,
learning,
R,
C,
delta_t,
threshold_rh,
threshold_r=-55):
I = I_function(time, step_size, scale)
neuron = ELIFPopulation(shape, spike_trace, additive_spike_trace, tau_s,
trace_scale, is_inhibitory, learning, R, C,
delta_t, threshold_rh, threshold_r)
neuron.dt = dt
monitor = Monitor(neuron, state_variables=["s", "u"])
monitor.set_time_steps(time, dt)
monitor.reset_state_variables()
for i in range(len(I)):
neuron.forward(I[i][0])
monitor.record()
return neuron, I, torch.transpose(monitor.get("s") * 1, 0,
1), monitor.get("u")
monitor1 = Monitor(n1, state_variables=["s"])
monitor1.set_time_steps(time, dt)
monitor1.reset_state_variables()
monitor2 = Monitor(n2, state_variables=["s"])
monitor2.set_time_steps(time, dt)
monitor2.reset_state_variables()
I_ex = 0
for i in range(len(I)):
n1.forward(I[i])
n2.forward(I_ex)
I_ex = con1.compute()
monitor1.record()
monitor2.record()
s1 = monitor1.get("s").flatten(start_dim=1)
s2 = monitor2.get("s").flatten(start_dim=1)
I = I.flatten(start_dim=1)
d1 = encoder.decode(s1.numpy(), shape1)
d2 = encoder.decode(s2.numpy(), shape2)
plot = plotting()
plot.plot_visual_activity(s2.T, d1, d2)
plot.show()
monitor1.reset_state_variables()
monitor2 = Monitor(pn2, state_variables=["s", "u"])
monitor2.set_time_steps(time, dt)
monitor2.reset_state_variables()
I_in = 0
I_self = 0
I_ex = 0
for i in range(len(I1)):
pn1.forward(I1[i] + I_self - I_in)
pn2.forward(I2[i] + I_ex)
I_self = con1.compute()
I_in = con2.compute()
I_ex = con3.compute()
monitor1.record()
monitor2.record()
s1 = torch.transpose(monitor1.get("s") * 1, 0, 1)
s2 = torch.transpose(monitor2.get("s") * 1, 0, 1)
plot = plotting()
plot.plot_population_activity_init(time / scale)
plot.plot_population_activity_update(s1, I1, mode="p1")
plot.plot_population_activity_update(s2,
I2,
start_idx=int(s1.numel() / s1.shape[-1]),
mode="p2")
plot.show()
out_ep2_ep2 = 0
for i in range(time):
ep1.forward(I_ep1[i] - out_ip1_ep1 + out_ep1_ep1)
ep2.forward(I_ep2[i] - out_ip1_ep2 + out_ep2_ep2)
ip1.forward(I_ip1[i] + out_ep1_ip1 + out_ep2_ip1)
out_ep1_ip1 = con_ep1_ip1.compute()
out_ep2_ip1 = con_ep2_ip1.compute()
out_ip1_ep1 = con_ip1_ep1.compute()
out_ip1_ep2 = con_ip1_ep2.compute()
out_ep1_ep1 = con_ep1_ep1.compute()
out_ep2_ep2 = con_ep2_ep2.compute()
monitor_ep1.record()
monitor_ep2.record()
monitor_ip1.record()
s_ep1 = torch.transpose(monitor_ep1.get("s") * 1, 0, 1)
s_ep2 = torch.transpose(monitor_ep2.get("s") * 1, 0, 1)
s_ip1 = torch.transpose(monitor_ip1.get("s") * 1, 0, 1)
plot = plotting()
plot.plot_three_population_activity_init(time / scale)
plot.plot_three_population_activity_update(s_ep1,
I_ep1,
s_ep2,
I_ep2,
s_ip1,
I_ip1,
n1="ep1",
n2="ep2",
n3="ip1")