Building Networks: Lapicque’s Neuron
Building a fully-connected network using Lapicque’s neuron model.
Example:
import torch
import torch.nn as nn
import snntorch as snn
beta = 0.5
R = 1
C = 1.44
batch_size = 128
num_inputs = 784
num_hidden = 1000
num_outputs = 10
num_steps = 100
# Define Network
class Net(nn.Module):
def __init__(self):
super().__init__()
# initialize layers
self.fc1 = nn.Linear(num_inputs, num_hidden)
self.lif1 = snn.Lapicque(beta=beta)
self.fc2 = nn.Linear(num_hidden, num_outputs)
self.lif2 = snn.Lapicque(R=R, C=C) # lif1 and lif2 are approximately equivalent
def forward(self, x, mem1, spk1, mem2):
for step in range(num_steps):
cur1 = self.fc1(x)
spk1, mem1 = self.lif1(cur1, mem1)
cur2 = self.fc2(spk1)
spk2, mem2 = self.lif2(cur2, mem2)
spk2_rec.append(spk2)
mem2_rec.append(mem2)
return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)
net = Net().to(device)
output, mem_rec = net(data.view(batch_size, -1))