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,
threshold=-55):
I = I_function(time, step_size, scale)
neuron = LIFPopulation(shape, spike_trace, additive_spike_trace, tau_s,
trace_scale, is_inhibitory, learning, R, C,
threshold)
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")
# encoder = Time2FirstSpikeEncoder(time = time)
encoder = PoissonEncoder(time = time, r = 10)
I = encoder(s)
# kernel = DoG_kernel(k=7, sigma1=0.1, sigma2=1)
kernel = gabor_kernel(k = 7, sigma = .4, teta = 0, gamma = .7, lambdal = .8)
n1 = InputPopulation(
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()
neuron_size = 100
shape1 = (int(neuron_size * 0.8), )
shape2 = (int(neuron_size * 0.2), )
# I1 = noise_function(time = time)
# I2 = I1
I1 = noise_function(time, shape1[0])
I2 = noise_function(time, shape2[0])
pn1 = LIFPopulation(shape=shape1,
spike_trace=True,
additive_spike_trace=True,
tau_s=10,
trace_scale=1.,
is_inhibitory=False,
learning=False,
R=1,
C=20,
threshold=-40,
dt=dt)
pn2 = LIFPopulation(shape=shape2,
spike_trace=True,
additive_spike_trace=True,
tau_s=10,
trace_scale=1.,
is_inhibitory=True,
learning=False,
R=1,
C=20,
from cnsproject.plotting.plotting import time_potential
from cnsproject.plotting.plotting import current_frequency
from cnsproject.utils import compute_current_frequency
## First set of parameters
## LIF Parameters
dt = 0.01
rest_potential = np.array([-70,-80,-60,-70,-70,-70,-70],)
threshold_potential = np.array([-55,-55,-55,-55,-55,-55,-55])
tau_m = np.array([1,1,1,0.1,10,1,1])
R = np.array([1,1,1,1,1,0.1,10])
## Create LIF based on parameters
lif_neuron = LIFPopulation(
spike_trace=False, additive_spike_trace=False, dt=dt, rest_potential=rest_potential,
threshold_potential=threshold_potential, tau_m=tau_m, R=R
).to(device=device,dtype=dtype)
## Compute frequency
plt.figure(figsize=(18,20))
current = torch.arange(1,31,device=device,dtype=dtype)
np_current = np.arange(1,31)
for i in range(len(R)):
model = LIFPopulation(
spike_trace=False, additive_spike_trace=False, dt=dt, rest_potential=rest_potential[i],
threshold_potential=threshold_potential[i], tau_m=tau_m[i], R=R[i]
).to(device=device,dtype=dtype)
frequencies = compute_current_frequency(current,model,dt)
plt.subplot(4,2,i+1)
shape_ep2 = (int(neuron_size), )
shape_ip1 = (int(neuron_size), )
# I_ep1 = incremental_step_noise_function(time = time, I_value = 100, scale = scale, neuron_size = shape_ep1[0], gap = 5)
# I_ep2 = incremental_step_noise_function(time = time, I_value = 100, scale = scale, neuron_size = shape_ep2[0], gap = 3)
# I_ip1 = step_noise_function(time = time, I_value = 100, scale = scale, neuron_size = shape_ip1[0])
I_ep1 = noise_function(time, shape_ep1[0])
I_ep2 = noise_function(time, shape_ep2[0])
I_ip1 = noise_function(time, shape_ip1[0]) * 0
ep1 = LIFPopulation(shape=shape_ep1,
spike_trace=True,
additive_spike_trace=True,
tau_s=10,
trace_scale=1.,
is_inhibitory=False,
learning=False,
R=1,
C=20,
threshold=-40,
dt=dt)
ep2 = LIFPopulation(shape=shape_ep2,
spike_trace=True,
additive_spike_trace=True,
tau_s=10,
trace_scale=1.,
is_inhibitory=False,
learning=False,
R=1,
C=20,
threshold=-40,