nu=[0, 1e-2],
norm=78.4)
# Voltage recording for excitatory and inhibitory layers.
exc_voltage_monitor = Monitor(network.layers["Ae"], ["v"], time=time)
inh_voltage_monitor = Monitor(network.layers["Ai"], ["v"], time=time)
network.add_monitor(exc_voltage_monitor, name="exc_voltage")
network.add_monitor(inh_voltage_monitor, name="inh_voltage")
# Load MNIST data.
images, labels = MNIST(path=os.path.join("..", "..", "data", "MNIST"),
download=True).get_train()
images = images.view(-1, 784)
images *= intensity
if gpu:
images = images.to("cuda")
labels = labels.to("cuda")
# Lazily encode data as Poisson spike trains.
data_loader = poisson_loader(data=images, time=time, dt=dt)
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
# Neuron assignments and spike proportions.
assignments = -torch.ones_like(torch.Tensor(n_neurons))
proportions = torch.zeros_like(torch.Tensor(n_neurons, 10))
rates = torch.zeros_like(torch.Tensor(n_neurons, 10))
# Sequence of accuracy estimates.
accuracy = {"all": [], "proportion": []}
nu=[0, 1e-2],
norm=78.4)
# Voltage recording for excitatory and inhibitory layers.
exc_voltage_monitor = Monitor(network.layers['Ae'], ['v'], time=time)
inh_voltage_monitor = Monitor(network.layers['Ai'], ['v'], time=time)
network.add_monitor(exc_voltage_monitor, name='exc_voltage')
network.add_monitor(inh_voltage_monitor, name='inh_voltage')
# Load MNIST data.
images, labels = MNIST(path=os.path.join('..', '..', 'data', 'MNIST'),
download=True).get_train()
images = images.view(-1, 784)
images *= intensity
if gpu:
images = images.to('cuda')
labels = labels.to('cuda')
# Lazily encode data as Poisson spike trains.
data_loader = poisson_loader(data=images, time=time, dt=dt)
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
# Neuron assignments and spike proportions.
assignments = -torch.ones_like(torch.Tensor(n_neurons))
proportions = torch.zeros_like(torch.Tensor(n_neurons, 10))
rates = torch.zeros_like(torch.Tensor(n_neurons, 10))
# Sequence of accuracy estimates.
accuracy = {'all': [], 'proportion': []}
