# Voltage recordings for excitatory and readout layers.
voltages = {}
for layer in set(layers.keys()) - {"X"}:
voltages[layer] = Monitor(layers[layer], ["v"], time=plot_interval)
# Add all layers and connections to the network.
for layer in layers:
network.add_layer(layers[layer], name=layer)
network.add_connection(input_exc_conn, source="X", target="E")
network.add_connection(exc_readout_conn, source="E", target="R")
# Add all monitors to the network.
for layer in layers:
network.add_monitor(spikes[layer], name="%s_spikes" % layer)
if layer in voltages:
network.add_monitor(voltages[layer], name="%s_voltages" % layer)
# Load the Breakout environment.
environment = GymEnvironment("BreakoutDeterministic-v4")
environment.reset()
pipeline = EnvironmentPipeline(
network,
environment,
encoding=bernoulli,
time=1,
history=5,
delta=10,
thresh=-52 + np.random.randn(n_neurons).astype(float))
network.add_layer(output, name="O")
C1 = Connection(source=inpt,
target=output,
w=0.5 * torch.randn(inpt.n, output.n))
C2 = Connection(source=output,
target=output,
w=0.5 * torch.randn(output.n, output.n))
network.add_connection(C1, source="I", target="O")
network.add_connection(C2, source="O", target="O")
spikes = {}
for l in network.layers:
spikes[l] = Monitor(network.layers[l], ["s"], time=time)
network.add_monitor(spikes[l], name="%s_spikes" % l)
voltages = {"O": Monitor(network.layers["O"], ["v"], time=time)}
network.add_monitor(voltages["O"], name="O_voltages")
# Directs network to GPU
if gpu:
network.to("cuda")
# Get MNIST training images and labels.
# Load MNIST data.
dataset = MNIST(
PoissonEncoder(time=time, dt=dt),
None,
root=os.path.join("..", "..", "data", "MNIST"),
download=True,