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")
shape = shape1, spike_trace = True, additive_spike_trace = True, tau_s = 4, trace_scale = 1.,
is_inhibitory = False, learning = False, R = 1, C = 20, threshold = -40
)
n1.dt = dt
n2 = LIFPopulation(
shape = shape2, spike_trace = True, additive_spike_trace = True, tau_s = 4, trace_scale = 1.,
is_inhibitory = False, learning = False, R = 1, C = 20, threshold = -40
)
n2.dt = dt
con1 = ConvolutionalConnection(
pre = n1, post = n2, kernel = kernel, J = 1000, padding = 0, stride = 1
)
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()
wmean=21.,
wstd=5.)
con3 = Connection(pre=pn1,
post=pn2,
lr=None,
weight_decay=0.0,
J=1,
tau_s=10,
trace_scale=1.,
dt=dt,
connectivity=randomNormalConnect,
wmean=20.,
wstd=5.)
monitor1 = Monitor(pn1, state_variables=["s", "u"])
monitor1.set_time_steps(time, dt)
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()
connectivity=randomNormalConnect,
wmean=20.,
wstd=5.)
con_ep2_ep2 = Connection(pre=ep2,
post=ep2,
lr=None,
weight_decay=0.0,
J=1,
tau_s=10,
trace_scale=1.,
dt=dt,
connectivity=randomNormalConnect,
wmean=20.,
wstd=5.)
monitor_ep1 = Monitor(ep1, state_variables=["s", "u"])
monitor_ep1.set_time_steps(time, dt)
monitor_ep1.reset_state_variables()
monitor_ep2 = Monitor(ep2, state_variables=["s", "u"])
monitor_ep2.set_time_steps(time, dt)
monitor_ep2.reset_state_variables()
monitor_ip1 = Monitor(ip1, state_variables=["s", "u"])
monitor_ip1.set_time_steps(time, dt)
monitor_ip1.reset_state_variables()
out_ep1_ip1 = 0
out_ep2_ip1 = 0
out_ip1_ep1 = 0
out_ip1_ep2 = 0