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': []}