def test_add_objects(self):
network = Network(dt=1.0, learning=False)
inpt = Input(100)
network.add_layer(inpt, name='X')
lif = LIFNodes(50)
network.add_layer(lif, name='Y')
assert inpt == network.layers['X']
assert lif == network.layers['Y']
conn = Connection(inpt, lif)
network.add_connection(conn, source='X', target='Y')
assert conn == network.connections[('X', 'Y')]
monitor = Monitor(lif, state_vars=['s', 'v'])
network.add_monitor(monitor, 'Y')
assert monitor == network.monitors['Y']
network.save('net.pt')
_network = load('net.pt', learning=True)
assert _network.learning
assert 'X' in _network.layers
assert 'Y' in _network.layers
assert ('X', 'Y') in _network.connections
assert 'Y' in _network.monitors
del _network
os.remove('net.pt')
def lif_feed_forward_benchmark(parameters: BenchmarkParameters):
T = parameters.dt * parameters.sequence_length
network = Network(batch_size=parameters.batch_size, dt=parameters.dt)
network.add_layer(Input(n=parameters.features), name="Input")
network.add_layer(LIFNodes(n=parameters.features), name="Neurons")
network.add_connection(
Connection(source=network.layers["Input"], target=network.layers["Neurons"]),
source="Input",
target="Neurons",
)
input_spikes = (
PoissonEncoder(time=T, dt=parameters.dt)(
0.3 * torch.ones(parameters.batch_size, parameters.features)
)
.to(parameters.device)
.float()
)
input_spikes.requires_grad = False
input_data = {"Input": input_spikes}
network.to(parameters.device)
for param in network.parameters():
param.requires_grad = False
start = time.time()
network.run(inputs=input_data, time=T)
end = time.time()
duration = end - start
return duration
def test_add_objects(self):
network = Network(dt=1.0, learning=False)
inpt = Input(100)
network.add_layer(inpt, name="X")
lif = LIFNodes(50)
network.add_layer(lif, name="Y")
assert inpt == network.layers["X"]
assert lif == network.layers["Y"]
conn = Connection(inpt, lif)
network.add_connection(conn, source="X", target="Y")
assert conn == network.connections[("X", "Y")]
monitor = Monitor(lif, state_vars=["s", "v"])
network.add_monitor(monitor, "Y")
assert monitor == network.monitors["Y"]
network.save("net.pt")
_network = load("net.pt", learning=True)
assert _network.learning
assert "X" in _network.layers
assert "Y" in _network.layers
assert ("X", "Y") in _network.connections
assert "Y" in _network.monitors
del _network
os.remove("net.pt")
def test_post_pre(self):
# Connection test
network = Network(dt=1.0)
network.add_layer(Input(n=100, traces=True), name='input')
network.add_layer(LIFNodes(n=100, traces=True), name='output')
network.add_connection(Connection(source=network.layers['input'],
target=network.layers['output'],
nu=1e-2,
update_rule=PostPre),
source='input',
target='output')
network.run(
inpts={'input': torch.bernoulli(torch.rand(250, 100)).byte()},
time=250)
# Conv2dConnection test
network = Network(dt=1.0)
network.add_layer(Input(shape=[1, 1, 10, 10], traces=True),
name='input')
network.add_layer(LIFNodes(shape=[1, 32, 8, 8], traces=True),
name='output')
network.add_connection(Conv2dConnection(
source=network.layers['input'],
target=network.layers['output'],
kernel_size=3,
stride=1,
nu=1e-2,
update_rule=PostPre),
source='input',
target='output')
network.run(inpts={
'input':
torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()
},
time=250)
def create_bindsnet(input_width, input_height, action_num=3):
from bindsnet.network import Network
from bindsnet.learning import MSTDP
from bindsnet.network.nodes import Input, LIFNodes
from bindsnet.network.topology import Connection
network = Network(dt=1.0)
# Layers of neurons.
inpt = Input(n=input_height * input_width,
shape=[input_height, input_width],
traces=True)
middle = LIFNodes(n=100, traces=True)
out = LIFNodes(n=action_num, refrac=0, traces=True)
# Connections between layers.
inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1e-1)
middle_out = Connection(source=middle,
target=out,
wmin=0,
wmax=1,
update_rule=MSTDP,
nu=1e-1,
norm=0.5 * middle.n)
# Add all layers and connections to the network.
network.add_layer(inpt, name='Input Layer')
network.add_layer(middle, name='Hidden Layer')
network.add_layer(out, name='Output Layer')
network.add_connection(inpt_middle,
source='Input Layer',
target='Hidden Layer')
network.add_connection(middle_out,
source='Hidden Layer',
target='Output Layer')
return network
def test_weight_dependent_post_pre(self):
# Connection test
network = Network(dt=1.0)
network.add_layer(Input(n=100, traces=True), name="input")
network.add_layer(LIFNodes(n=100, traces=True), name="output")
network.add_connection(
Connection(
source=network.layers["input"],
target=network.layers["output"],
nu=1e-2,
update_rule=WeightDependentPostPre,
wmin=-1,
wmax=1,
),
source="input",
target="output",
)
network.run(
inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
time=250,
)
# Conv2dConnection test
network = Network(dt=1.0)
network.add_layer(Input(shape=[1, 10, 10], traces=True), name="input")
network.add_layer(
LIFNodes(shape=[32, 8, 8], traces=True), name="output"
)
network.add_connection(
Conv2dConnection(
source=network.layers["input"],
target=network.layers["output"],
kernel_size=3,
stride=1,
nu=1e-2,
update_rule=WeightDependentPostPre,
wmin=-1,
wmax=1,
),
source="input",
target="output",
)
network.run(
inputs={
"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()
},
time=250,
)
def test_rmax(self):
# Connection test
network = Network(dt=1.0)
network.add_layer(Input(n=100, traces=True, traces_additive=True),
name='input')
network.add_layer(SRM0Nodes(n=100), name='output')
network.add_connection(Connection(source=network.layers['input'],
target=network.layers['output'],
nu=1e-2,
update_rule=Rmax),
source='input',
target='output')
network.run(
inpts={'input': torch.bernoulli(torch.rand(250, 100)).byte()},
time=250,
reward=1.)
def test_add_objects(self):
network = Network(dt=1.0)
inpt = Input(100); network.add_layer(inpt, name='X')
lif = LIFNodes(50); network.add_layer(lif, name='Y')
assert inpt == network.layers['X']
assert lif == network.layers['Y']
conn = Connection(inpt, lif); network.add_connection(conn, source='X', target='Y')
assert conn == network.connections[('X', 'Y')]
monitor = Monitor(lif, state_vars=['s', 'v']); network.add_monitor(monitor, 'Y')
assert monitor == network.monitors['Y']
def test_mstdpet(self):
# Connection test
network = Network(dt=1.0)
network.add_layer(Input(n=100), name="input")
network.add_layer(LIFNodes(n=100), name="output")
network.add_connection(
Connection(
source=network.layers["input"],
target=network.layers["output"],
nu=1e-2,
update_rule=MSTDPET,
),
source="input",
target="output",
)
network.run(
inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
time=250,
reward=1.0,
)
# Conv2dConnection test
network = Network(dt=1.0)
network.add_layer(Input(shape=[1, 10, 10]), name="input")
network.add_layer(LIFNodes(shape=[32, 8, 8]), name="output")
network.add_connection(
Conv2dConnection(
source=network.layers["input"],
target=network.layers["output"],
kernel_size=3,
stride=1,
nu=1e-2,
update_rule=MSTDPET,
),
source="input",
target="output",
)
network.run(
inputs={
"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()
},
time=250,
reward=1.0,
)
def test_hebbian(self):
# Connection test
network = Network(dt=1.0)
network.add_layer(Input(n=100, traces=True), name="input")
network.add_layer(LIFNodes(n=100, traces=True), name="output")
network.add_connection(
Connection(
source=network.layers["input"],
target=network.layers["output"],
nu=1e-2,
update_rule=Hebbian,
),
source="input",
target="output",
)
network.run(
inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
time=250,
)
# Conv2dConnection test
network = Network(dt=1.0)
network.add_layer(Input(shape=[1, 10, 10], traces=True), name="input")
network.add_layer(
LIFNodes(shape=[32, 8, 8], traces=True), name="output"
)
network.add_connection(
Conv2dConnection(
source=network.layers["input"],
target=network.layers["output"],
kernel_size=3,
stride=1,
nu=1e-2,
update_rule=Hebbian,
),
source="input",
target="output",
)
# shape is [time, batch, channels, height, width]
network.run(
inputs={
"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()
},
time=250,
)
def BindsNET_cpu(n_neurons, time):
t0 = t()
torch.set_default_tensor_type('torch.FloatTensor')
t1 = t()
network = Network()
network.add_layer(Input(n=n_neurons), name='X')
network.add_layer(LIFNodes(n=n_neurons), name='Y')
network.add_connection(Connection(source=network.layers['X'],
target=network.layers['Y']),
source='X',
target='Y')
data = {'X': poisson(datum=torch.rand(n_neurons), time=time)}
network.run(inpts=data, time=time)
return t() - t0, t() - t1
def BindsNET_cpu(n_neurons, time):
t0 = t()
torch.set_default_tensor_type("torch.FloatTensor")
t1 = t()
network = Network()
network.add_layer(Input(n=n_neurons), name="X")
network.add_layer(LIFNodes(n=n_neurons), name="Y")
network.add_connection(
Connection(source=network.layers["X"], target=network.layers["Y"]),
source="X",
target="Y",
)
data = {"X": poisson(datum=torch.rand(n_neurons), time=time)}
network.run(inputs=data, time=time)
return t() - t0, t() - t1
def __init__(self, parameters: BenchmarkParameters):
super(BindsNetModule, self).__init__()
network = Network(batch_size=parameters.batch_size, dt=parameters.dt)
lif_nodes = LIFNodes(n=parameters.features)
monitor = Monitor(obj=lif_nodes,
state_vars=("s"),
time=parameters.sequence_length)
network.add_layer(Input(n=parameters.features), name="Input")
network.add_layer(lif_nodes, name="Neurons")
network.add_connection(
Connection(source=network.layers["Input"],
target=network.layers["Neurons"]),
source="Input",
target="Neurons",
)
network.add_monitor(monitor, "Monitor")
network.to(parameters.device)
self.parameters = parameters
self.network = network
self.monitor = monitor
def main(n_input=1, n_output=10, time=1000):
# Network building.
network = Network(dt=1.0)
input_layer = RealInput(n=n_input)
output_layer = LIFNodes(n=n_output)
connection = Connection(source=input_layer, target=output_layer)
monitor = Monitor(obj=output_layer, state_vars=('v', ), time=time)
# Adding network components.
network.add_layer(input_layer, name='X')
network.add_layer(output_layer, name='Y')
network.add_connection(connection, source='X', target='Y')
network.add_monitor(monitor, name='X_monitor')
# Creating real-valued inputs and running simulation.
inpts = {'X': torch.ones(time, n_input)}
network.run(inpts=inpts, time=time)
# Plot voltage activity.
plt.plot(monitor.get('v').numpy().T)
plt.show()
from bindsnet.network.monitors import Monitor
from bindsnet.network.nodes import LIFNodes
from bindsnet.network.topology import Connection
from bindsnet.utils import get_square_weights
network = Network(dt=1.0)
inpt = Input(784, shape=(28, 28))
network.add_layer(inpt, name="I")
output = LIFNodes(625, thresh=-52 + torch.randn(625))
network.add_layer(output, name="O")
C1 = Connection(source=inpt, target=output, w=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=250)
network.add_monitor(spikes[l], name="%s_spikes" % l)
voltages = {"O": Monitor(network.layers["O"], ["v"], time=250)}
network.add_monitor(voltages["O"], name="O_voltages")
# Get MNIST training images and labels.
images, labels = MNIST(path="../../data/MNIST", download=True).get_train()
images *= 0.25
# Create lazily iterating Poisson-distributed data loader.
def main(args):
if args.gpu:
torch.cuda.manual_seed_all(args.seed)
else:
torch.manual_seed(args.seed)
conv_size = int(
(28 - args.kernel_size + 2 * args.padding) / args.stride) + 1
# Build network.
network = Network()
input_layer = Input(n=784, shape=(1, 28, 28), traces=True)
conv_layer = DiehlAndCookNodes(
n=args.n_filters * conv_size * conv_size,
shape=(args.n_filters, conv_size, conv_size),
traces=True,
)
conv_conn = Conv2dConnection(
input_layer,
conv_layer,
kernel_size=args.kernel_size,
stride=args.stride,
update_rule=PostPre,
norm=0.4 * args.kernel_size**2,
nu=[0, args.lr],
reduction=max_without_indices,
wmax=1.0,
)
w = torch.zeros(args.n_filters, conv_size, conv_size, args.n_filters,
conv_size, conv_size)
for fltr1 in range(args.n_filters):
for fltr2 in range(args.n_filters):
if fltr1 != fltr2:
for i in range(conv_size):
for j in range(conv_size):
w[fltr1, i, j, fltr2, i, j] = -100.0
w = w.view(args.n_filters * conv_size * conv_size,
args.n_filters * conv_size * conv_size)
recurrent_conn = Connection(conv_layer, conv_layer, w=w)
network.add_layer(input_layer, name="X")
network.add_layer(conv_layer, name="Y")
network.add_connection(conv_conn, source="X", target="Y")
network.add_connection(recurrent_conn, source="Y", target="Y")
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers["Y"], ["v"], time=args.time)
network.add_monitor(voltage_monitor, name="output_voltage")
if args.gpu:
network.to("cuda")
# Load MNIST data.
train_dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=["s"],
time=args.time)
network.add_monitor(spikes[layer], name="%s_spikes" % layer)
voltages = {}
for layer in set(network.layers) - {"X"}:
voltages[layer] = Monitor(network.layers[layer],
state_vars=["v"],
time=args.time)
network.add_monitor(voltages[layer], name="%s_voltages" % layer)
# Train the network.
print("Begin training.\n")
start = time()
weights_im = None
for epoch in range(args.n_epochs):
if epoch % args.progress_interval == 0:
print("Progress: %d / %d (%.4f seconds)" %
(epoch, args.n_epochs, time() - start))
start = time()
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=args.gpu,
)
for step, batch in enumerate(tqdm(train_dataloader)):
# Get next input sample.
inpts = {"X": batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input.
network.run(inpts=inpts, time=args.time, input_time_dim=0)
# Decay learning rate.
network.connections["X", "Y"].nu[1] *= 0.99
# Optionally plot various simulation information.
if args.plot:
weights = conv_conn.w
weights_im = plot_conv2d_weights(weights, im=weights_im)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print("Progress: %d / %d (%.4f seconds)\n" %
(args.n_epochs, args.n_epochs, time() - start))
print("Training complete.\n")
target=tnn_layer_1,
w=0 * torch.rand(tnn_layer_1.n, tnn_layer_1.n),
update_rule=TNN_STDP,
ucapture=10 / 128,
uminus=10 / 128,
usearch=2 / 128,
ubackoff=96 / 128,
umin=4 / 128,
timesteps=num_timesteps,
maxweight=max_weight)
network.add_layer(input_layer, name="I")
network.add_layer(tnn_layer_1, name="TNN_1")
network.add_layer(buffer_layer_1, name="BUF")
# network.add_connection(C2, source="TNN_1", target="TNN_1")
network.add_connection(buf_to_TNN, source="BUF", target="TNN_1")
network.add_connection(TNN_to_buf, source="TNN_1", target="BUF")
network.add_connection(C1, source="I", target="TNN_1")
spikes = {}
for l in network.layers:
spikes[l] = Monitor(network.layers[l], ["s"], time=num_timesteps)
network.add_monitor(spikes[l], name="%s_spikes" % l)
dataset = MNIST(
RampNoLeakTNNEncoder(time=num_timesteps, dt=1),
None,
root=os.path.join("..", "..", "data", "MNIST"),
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
# Spike recordings for all layers.
spikes = {}
for layer in layers:
spikes[layer] = Monitor(layers[layer], ['s'], time=plot_interval)
# 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 SpaceInvaders environment.
environment = GymEnvironment('Asteroids-v0')
environment.reset()
pipeline = Pipeline(network, environment, encoding=bernoulli, time=1, history=5, delta=10, plot_interval=plot_interval,
print_interval=print_interval, render_interval=render_interval, action_function=select_multinomial,
def toLIF(network: Network):
new_network = Network(dt=1, learning=True)
input_layer = Input(n=network.X.n,
shape=network.X.shape,
traces=True,
tc_trace=network.X.tc_trace.item())
exc_layer = LIFNodes(
n=network.Ae.n,
traces=True,
rest=network.Ai.rest.item(),
reset=network.Ai.reset.item(),
thresh=network.Ai.thresh.item(),
refrac=network.Ai.refrac.item(),
tc_decay=network.Ai.tc_decay.item(),
)
inh_layer = LIFNodes(
n=network.Ai.n,
traces=False,
rest=network.Ai.rest.item(),
reset=network.Ai.reset.item(),
thresh=network.Ai.thresh.item(),
tc_decay=network.Ai.tc_decay.item(),
refrac=network.Ai.refrac.item(),
)
# Connections
w = network.X_to_Ae.w
input_exc_conn = Connection(
source=input_layer,
target=exc_layer,
w=w,
update_rule=PostPre,
nu=network.X_to_Ae.nu,
reduction=network.X_to_Ae.reduction,
wmin=network.X_to_Ae.wmin,
wmax=network.X_to_Ae.wmax,
norm=network.X_to_Ae.norm * 1,
)
w = network.Ae_to_Ai.w
exc_inh_conn = Connection(source=exc_layer,
target=inh_layer,
w=w,
wmin=network.Ae_to_Ai.wmin,
wmax=network.Ae_to_Ai.wmax)
w = network.Ai_to_Ae.w
inh_exc_conn = Connection(source=inh_layer,
target=exc_layer,
w=w,
wmin=network.Ai_to_Ae.wmin,
wmax=network.Ai_to_Ae.wmax)
# Add to network
new_network.add_layer(input_layer, name="X")
new_network.add_layer(exc_layer, name="Ae")
new_network.add_layer(inh_layer, name="Ai")
new_network.add_connection(input_exc_conn, source="X", target="Ae")
new_network.add_connection(exc_inh_conn, source="Ae", target="Ai")
new_network.add_connection(inh_exc_conn, source="Ai", target="Ae")
exc_voltage_monitor = Monitor(new_network.layers["Ae"], ["v"], time=500)
inh_voltage_monitor = Monitor(new_network.layers["Ai"], ["v"], time=500)
new_network.add_monitor(exc_voltage_monitor, name="exc_voltage")
new_network.add_monitor(inh_voltage_monitor, name="inh_voltage")
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(new_network.layers[layer],
state_vars=["s"],
time=time)
new_network.add_monitor(spikes[layer], name="%s_spikes" % layer)
return new_network
out = LIFNodes(n=60, refrac=0, traces=True, thresh=-40.0)
# Connections between layers.
inpt_middle = Connection(source=inpt, target=middle, wmax=1e-2)
middle_out = Connection(source=middle,
target=out,
wmax=0.5,
update_rule=m_stdp_et,
nu=2e-2,
norm=0.15 * middle.n)
# Add all layers and connections to the network.
network.add_layer(inpt, name='X')
network.add_layer(middle, name='Y')
network.add_layer(out, name='Z')
network.add_connection(inpt_middle, source='X', target='Y')
network.add_connection(middle_out, source='Y', target='Z')
# Load SpaceInvaders environment.
environment = GymEnvironment('SpaceInvaders-v0')
environment.reset()
# Build pipeline from specified components.
pipeline = Pipeline(network,
environment,
encoding=bernoulli,
feedback=select_multinomial,
output='Z',
time=1,
history_length=2,
delta=4,
def ann_to_snn(ann: Union[nn.Module, str],
input_shape: Sequence[int],
data: Optional[torch.Tensor] = None,
percentile: float = 99.9,
node_type: Optional[nodes.Nodes] = SubtractiveResetIFNodes,
**kwargs) -> Network:
# language=rst
"""
Converts an artificial neural network (ANN) written as a ``torch.nn.Module`` into a near-equivalent spiking neural
network.
:param ann: Artificial neural network implemented in PyTorch. Accepts either ``torch.nn.Module`` or path to network
saved using ``torch.save()``.
:param input_shape: Shape of input data.
:param data: Data to use to perform data-based weight normalization of shape ``[n_examples, ...]``.
:param percentile: Percentile (in ``[0, 100]``) of activations to scale by in data-based normalization scheme.
:return: Spiking neural network implemented in PyTorch.
"""
if isinstance(ann, str):
ann = torch.load(ann)
else:
ann = deepcopy(ann)
assert isinstance(ann, nn.Module)
if data is None:
import warnings
warnings.warn("Data is None. Weights will not be scaled.",
RuntimeWarning)
else:
ann = data_based_normalization(ann=ann,
data=data.detach(),
percentile=percentile)
snn = Network()
input_layer = nodes.RealInput(shape=input_shape)
snn.add_layer(input_layer, name="Input")
children = []
for c in ann.children():
if isinstance(c, nn.Sequential):
for c2 in list(c.children()):
children.append(c2)
else:
children.append(c)
i = 0
prev = input_layer
while i < len(children) - 1:
current, nxt = children[i:i + 2]
layer, connection = _ann_to_snn_helper(prev, current, node_type,
**kwargs)
i += 1
if layer is None or connection is None:
continue
snn.add_layer(layer, name=str(i))
snn.add_connection(connection, source=str(i - 1), target=str(i))
prev = layer
current = children[-1]
layer, connection = _ann_to_snn_helper(prev, current, node_type, **kwargs)
i += 1
if layer is not None or connection is not None:
snn.add_layer(layer, name=str(i))
snn.add_connection(connection, source=str(i - 1), target=str(i))
return snn
# w_recur[torch.rand(rtnn_layer_1.n, rtnn_layer_1.n) < 0.90] = 0
# buf1_to_rTNN = Connection(
# source=buffer_layer_1,
# target=rtnn_layer_1,
# w = w_recur,
# timesteps = num_timesteps,
# update_rule=None )
# Add all nodes to network:
network.add_layer(input_layer_a, name="I_a")
network.add_layer(rtnn_layer_1, name="rTNN_1")
# network.add_layer(buffer_layer_1, name="BUF_1")
# Add connections to network:
# (feedforward)
network.add_connection(FF1a, source="I_a", target="rTNN_1")
# (Recurrences)
# network.add_connection(rTNN_to_buf1, source="rTNN_1", target="BUF_1")
# network.add_connection(buf1_to_rTNN, source="BUF_1", target="rTNN_1")
# End of network creation
# Monitors:
spikes = {}
for l in network.layers:
spikes[l] = Monitor(network.layers[l], ["s"], time=num_timesteps)
network.add_monitor(spikes[l], name="%s_spikes" % l)
# Data and initial encoding:
dataset = MNIST(
RampNoLeakTNNEncoder(time=num_timesteps, dt=1),
w=-0.1 * torch.ones(PK.n, DCN.n))
PK_DCN_Anti = Connection(source=PK_Anti,
target=DCN_Anti,
w_max=0,
w=-0.1 * torch.ones(PK_Anti.n, DCN_Anti.n))
network.add_layer(layer=GR_Joint_layer, name="GR_Joint_layer")
network.add_layer(layer=PK, name="PK")
network.add_layer(layer=PK_Anti, name="PK_Anti")
network.add_layer(layer=IO, name="IO")
network.add_layer(layer=IO_Anti, name="IO_Anti")
network.add_layer(layer=DCN, name="DCN")
network.add_layer(layer=DCN_Anti, name="DCN_Anti")
network.add_connection(connection=Parallelfiber,
source="GR_Joint_layer",
target="PK")
network.add_connection(connection=Parallelfiber_Anti,
source="GR_Joint_layer",
target="PK_Anti")
network.add_connection(connection=Climbingfiber, source="IO", target="PK")
network.add_connection(connection=Climbingfiber_Anti,
source="IO_Anti",
target="PK_Anti")
network.add_connection(connection=PK_DCN, source="PK", target="DCN")
network.add_connection(connection=PK_DCN_Anti,
source="PK_Anti",
target="DCN_Anti")
GR_monitor = Monitor(obj=GR_Joint_layer, state_vars=("s"), time=time)
PK_monitor = Monitor(obj=PK, state_vars=("s", "v"), time=time)
w = 0.5 * max_weight * torch.rand(rtnn_layer_1.n, rtnn_layer_1.n),
timesteps = num_timesteps,
update_rule=TNN_STDP, **stdp_rtnn_params )
# Add all nodes to network:
network.add_layer(input_layer_a, name="I_a")
network.add_layer(input_layer_b, name="I_b")
network.add_layer(tnn_layer_1a, name="TNN_1a")
network.add_layer(tnn_layer_1b, name="TNN_1b")
network.add_layer(buffer_layer_1, name="BUF_1")
# network.add_layer(buffer_layer_2, name="BUF_2")
network.add_layer(rtnn_layer_1, name="rTNN_1")
# Add connections to network:
# (feedforward)
network.add_connection(FF1a, source="I_a", target="TNN_1a")
network.add_connection(FF1b, source="I_b", target="TNN_1b")
network.add_connection(FF2a, source="TNN_1a", target="rTNN_1")
network.add_connection(FF2b, source="TNN_1b", target="rTNN_1")
# (Recurrences)
network.add_connection(rTNN_to_buf1, source="rTNN_1", target="BUF_1")
# network.add_connection(buf1_to_buf2, source="BUF_1", target="BUF_2")
network.add_connection(buf1_to_rTNN, source="BUF_1", target="rTNN_1")
# network.add_connection(buf2_to_rTNN, source="BUF_2", target="rTNN_1")
# End of network creation
# Monitors:
spikes = {}
for l in network.layers:
inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1e-1)
middle_out = Connection(
source=middle,
target=out,
wmin=0,
wmax=1,
update_rule=MSTDP,
nu=1e-1,
norm=0.5 * middle.n,
)
# Add all layers and connections to the network.
network.add_layer(inpt, name="Input Layer")
network.add_layer(middle, name="Hidden Layer")
network.add_layer(out, name="Output Layer")
network.add_connection(inpt_middle, source="Input Layer", target="Hidden Layer")
network.add_connection(middle_out, source="Hidden Layer", target="Output Layer")
# Load the Breakout environment.
environment = GymEnvironment("BreakoutDeterministic-v4")
environment.reset()
# Build pipeline from specified components.
environment_pipeline = EnvironmentPipeline(
network,
environment,
encoding=bernoulli,
action_function=select_softmax,
output="Output Layer",
time=100,
history_length=1,
w = torch.zeros(n_filters, conv_size, conv_size, n_filters, conv_size,
conv_size)
for fltr1 in range(n_filters):
for fltr2 in range(n_filters):
if fltr1 != fltr2:
for i in range(conv_size):
for j in range(conv_size):
w[fltr1, i, j, fltr2, i, j] = -100.0
w = w.view(n_filters * conv_size * conv_size,
n_filters * conv_size * conv_size)
recurrent_conn = Connection(conv_layer, conv_layer, w=w)
network.add_layer(input_layer, name="X")
network.add_layer(conv_layer, name="Y")
network.add_connection(conv_conn, source="X", target="Y")
network.add_connection(recurrent_conn, source="Y", target="Y")
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers["Y"], ["v"], time=time)
network.add_monitor(voltage_monitor, name="output_voltage")
if gpu:
network.to("cuda")
# Load MNIST data.
train_dataset = MNIST(
PoissonEncoder(time=time, dt=dt),
None,
"../../data/MNIST",
download=True,
def main(seed=0,
n_train=60000,
n_test=10000,
kernel_size=(16, ),
stride=(4, ),
n_filters=25,
padding=0,
inhib=100,
time=25,
lr=1e-3,
lr_decay=0.99,
dt=1,
intensity=1,
progress_interval=10,
update_interval=250,
plot=False,
train=True,
gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, n_train, kernel_size, stride, n_filters, padding, inhib, time,
lr, lr_decay, dt, intensity, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, n_train, n_test, kernel_size, stride, n_filters, padding,
inhib, time, lr, lr_decay, dt, intensity, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
n_examples = n_train if train else n_test
input_shape = [20, 20]
if kernel_size == input_shape:
conv_size = [1, 1]
else:
conv_size = (int((input_shape[0] - kernel_size[0]) / stride[0]) + 1,
int((input_shape[1] - kernel_size[1]) / stride[1]) + 1)
n_classes = 10
n_neurons = n_filters * np.prod(conv_size)
total_kernel_size = int(np.prod(kernel_size))
total_conv_size = int(np.prod(conv_size))
# Build network.
if train:
network = Network()
input_layer = Input(n=400, shape=(1, 1, 20, 20), traces=True)
conv_layer = DiehlAndCookNodes(n=n_filters * total_conv_size,
shape=(1, n_filters, *conv_size),
thresh=-64.0,
traces=True,
theta_plus=0.05 * (kernel_size[0] / 20),
refrac=0)
conv_layer2 = LIFNodes(n=n_filters * total_conv_size,
shape=(1, n_filters, *conv_size),
refrac=0)
conv_conn = Conv2dConnection(input_layer,
conv_layer,
kernel_size=kernel_size,
stride=stride,
update_rule=WeightDependentPostPre,
norm=0.05 * total_kernel_size,
nu=[0, lr],
wmin=0,
wmax=0.25)
conv_conn2 = Conv2dConnection(input_layer,
conv_layer2,
w=conv_conn.w,
kernel_size=kernel_size,
stride=stride,
update_rule=None,
wmax=0.25)
w = -inhib * torch.ones(n_filters, conv_size[0], conv_size[1],
n_filters, conv_size[0], conv_size[1])
for f in range(n_filters):
for f2 in range(n_filters):
if f != f2:
w[f, :, :f2, :, :] = 0
w = w.view(n_filters * conv_size[0] * conv_size[1],
n_filters * conv_size[0] * conv_size[1])
recurrent_conn = Connection(conv_layer, conv_layer, w=w)
network.add_layer(input_layer, name='X')
network.add_layer(conv_layer, name='Y')
network.add_layer(conv_layer2, name='Y_')
network.add_connection(conv_conn, source='X', target='Y')
network.add_connection(conv_conn2, source='X', target='Y_')
network.add_connection(recurrent_conn, source='Y', target='Y')
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'],
nu=network.connections['X', 'Y'].nu)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
# Load MNIST data.
dataset = MNIST(data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images *= intensity
images = images[:, 4:-4, 4:-4].contiguous()
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
full_spike_record = torch.zeros(n_examples, n_neurons)
# Neuron assignments and spike proportions.
if train:
logreg_model = LogisticRegression(warm_start=True,
n_jobs=-1,
solver='lbfgs',
max_iter=1000,
multi_class='multinomial')
logreg_model.coef_ = np.zeros([n_classes, n_neurons])
logreg_model.intercept_ = np.zeros(n_classes)
logreg_model.classes_ = np.arange(n_classes)
else:
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
logreg_coef, logreg_intercept = torch.load(open(path, 'rb'))
logreg_model = LogisticRegression(warm_start=True,
n_jobs=-1,
solver='lbfgs',
max_iter=1000,
multi_class='multinomial')
logreg_model.coef_ = logreg_coef
logreg_model.intercept_ = logreg_intercept
logreg_model.classes_ = np.arange(n_classes)
# Sequence of accuracy estimates.
curves = {'logreg': []}
predictions = {scheme: torch.Tensor().long() for scheme in curves.keys()}
if train:
best_accuracy = 0
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_ims = None
inpt_axes = None
spike_ims = None
spike_axes = None
weights_im = None
plot_update_interval = 100
start = t()
for i in range(n_examples):
if i % progress_interval == 0:
print('Progress: %d / %d (%.4f seconds)' %
(i, n_examples, t() - start))
start = t()
if i % update_interval == 0 and i > 0:
if train:
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
current_record = full_spike_record[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i %
len(labels)]
current_record = full_spike_record[i % len(labels) -
update_interval:i %
len(labels)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
full_spike_record=current_record,
logreg=logreg_model)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat(
[predictions[scheme], preds[scheme]], -1)
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print(
'New best accuracy! Saving network parameters to disk.'
)
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(
params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((logreg_model.coef_, logreg_model.intercept_),
open(path, 'wb'))
best_accuracy = max([x[-1] for x in curves.values()])
# Refit logistic regression model.
logreg_model = logreg_fit(full_spike_record[:i], labels[:i],
logreg_model)
print()
# Get next input sample.
image = images[i % len(images)]
sample = bernoulli(datum=image, time=time, dt=dt,
max_prob=1).unsqueeze(1).unsqueeze(1)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
network.connections['X', 'Y_'].w = network.connections['X', 'Y'].w
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y_'].get('s').view(
time, -1)
full_spike_record[i] = spikes['Y_'].get('s').view(time, -1).sum(0)
# Optionally plot various simulation information.
if plot and i % plot_update_interval == 0:
_input = inpts['X'].view(time, 400).sum(0).view(20, 20)
w = network.connections['X', 'Y'].w
_spikes = {
'X': spikes['X'].get('s').view(400, time),
'Y': spikes['Y'].get('s').view(n_filters * total_conv_size,
time),
'Y_': spikes['Y_'].get('s').view(n_filters * total_conv_size,
time)
}
inpt_axes, inpt_ims = plot_input(image.view(20, 20),
_input,
label=labels[i % len(labels)],
ims=inpt_ims,
axes=inpt_axes)
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_conv2d_weights(
w, im=weights_im, wmax=network.connections['X', 'Y'].wmax)
plt.pause(1e-2)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
current_record = full_spike_record[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i %
len(labels)]
current_record = full_spike_record[i % len(labels) -
update_interval:i % len(labels)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
full_spike_record=current_record,
logreg=logreg_model)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat([predictions[scheme], preds[scheme]],
-1)
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print('New best accuracy! Saving network parameters to disk.')
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((logreg_model.coef_, logreg_model.intercept_),
open(path, 'wb'))
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
print('Average accuracies:\n')
for scheme in curves.keys():
print('\t%s: %.2f' % (scheme, float(np.mean(curves[scheme]))))
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
to_write = [str(x) for x in to_write]
f = '_'.join(to_write) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
# Save results to disk.
results = [np.mean(curves['logreg']), np.std(curves['logreg'])]
to_write = params + results if train else test_params + results
to_write = [str(x) for x in to_write]
name = 'train.csv' if train else 'test.csv'
if not os.path.isfile(os.path.join(results_path, name)):
with open(os.path.join(results_path, name), 'w') as f:
if train:
columns = [
'seed', 'n_train', 'kernel_size', 'stride', 'n_filters',
'padding', 'inhib', 'time', 'lr', 'lr_decay', 'dt',
'intensity', 'update_interval', 'mean_logreg', 'std_logreg'
]
header = ','.join(columns) + '\n'
f.write(header)
else:
columns = [
'seed', 'n_train', 'n_test', 'kernel_size', 'stride',
'n_filters', 'padding', 'inhib', 'time', 'lr', 'lr_decay',
'dt', 'intensity', 'update_interval', 'mean_logreg',
'std_logreg'
]
header = ','.join(columns) + '\n'
f.write(header)
with open(os.path.join(results_path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if labels.numel() > n_examples:
labels = labels[:n_examples]
else:
while labels.numel() < n_examples:
if 2 * labels.numel() > n_examples:
labels = torch.cat(
[labels, labels[:n_examples - labels.numel()]])
else:
labels = torch.cat([labels, labels])
# Compute confusion matrices and save them to disk.
confusions = {}
for scheme in predictions:
confusions[scheme] = confusion_matrix(labels, predictions[scheme])
to_write = ['train'] + params if train else ['test'] + test_params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusions, os.path.join(confusion_path, f))
def Translate_Into_Networks(input_N, Shape, Output_N, Weight):
network_list = []
path = "gene/"
file_list = os.listdir(path)
gene_file_check = [file for file in file_list if file.endswith(".txt")]
if len(gene_file_check) == 0:
import startup
Gene_List = Genetic.Read_Gene()
for i in range(len(Gene_List)):
network = Network()
Decoded_List = []
Decoded_DNA_List = []
for j in range(len(Gene_List[i])):
Decoded_Gene = Gene_List[i][j].split('-')
if (Decoded_Gene[3] == 'F'):
pass
else:
if Decoded_Gene[1] == '~':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 0])
elif Decoded_Gene[1] == '!':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 1])
elif Decoded_Gene[1] == '@':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 2])
elif Decoded_Gene[1] == '#':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 3])
elif Decoded_Gene[1] == '$':
Decoded_List.append(
[int(Decoded_Gene[0]),
int(Decoded_Gene[2]), 4])
else:
print("THE GENOTYPE VALUE IS UNVALID")
raise ValueError
Decoded_DNA_List.append(Decoded_List)
Decoded_RNA_List: list = Decoded_DNA_List.copy()
for decoded_dna_idx, decoded_dna in enumerate(Decoded_DNA_List):
Gene_NUM = len(decoded_dna)
for k in range(Gene_NUM):
a = Decoded_DNA_List[decoded_dna_idx][k]
for l in range(k, Gene_NUM):
b = Decoded_DNA_List[decoded_dna_idx][l]
if a and b == 1:
if decoded_dna[k][2] == 0:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 1:
if decoded_dna[l][2] < 1:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 2:
if decoded_dna[l][2] < 2:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 3:
if decoded_dna[l][2] < 3:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
elif decoded_dna[k][2] == 4:
if decoded_dna[l][2] >= 4:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[l])
else:
Decoded_RNA_List[decoded_dna_idx].remove(
decoded_dna[k])
else:
pass
else:
pass
for Decoded_RNA in Decoded_RNA_List:
layer_list = {}
for m in range(len(Decoded_RNA)):
for n in range(m, len(Decoded_RNA)):
if Decoded_RNA[m][1] == Decoded_RNA[n][0]:
if Decoded_RNA[n][2] == 0:
layer_list[Decoded_RNA[m][0]] = nodes.IFNodes(
n=1, traces=True)
elif Decoded_RNA[n][2] == 1:
layer_list[Decoded_RNA[m][0]] = nodes.LIFNodes(
n=1, traces=True)
elif Decoded_RNA[n][2] == 2:
layer_list[Decoded_RNA[m]
[0]] = nodes.McCullochPitts(n=1,
traces=True)
elif Decoded_RNA[n][2] == 3:
layer_list[Decoded_RNA[m]
[0]] = nodes.IzhikevichNodes(
n=1, traces=True)
elif Decoded_RNA[n][2] == 4:
layer_list[Decoded_RNA[m][0]] = nodes.SRM0Nodes(
n=1, traces=True)
else:
print("UNVALID GENO_NEURON CODE")
raise ValueError
elif n == len(Decoded_List) - 1:
layer_list[Decoded_RNA[m][1]] = nodes.LIFNodes(n=1)
for l in range(len(Decoded_RNA)):
if not Decoded_RNA[l][0] in layer_list:
if Decoded_RNA[l][2] == 0:
layer_list[Decoded_RNA[l][0]] = nodes.IFNodes(
n=1, traces=True)
elif Decoded_RNA[l][2] == 1:
layer_list[Decoded_RNA[l][0]] = nodes.LIFNodes(
n=1, traces=True)
elif Decoded_RNA[l][2] == 2:
layer_list[Decoded_RNA[l][0]] = nodes.McCullochPitts(
n=1, traces=True)
elif Decoded_RNA[l][2] == 3:
layer_list[Decoded_RNA[l][0]] = nodes.IzhikevichNodes(
n=1, traces=True)
elif Decoded_RNA[l][2] == 4:
layer_list[Decoded_RNA[l][0]] = nodes.SRM0Nodes(
n=1, traces=True)
Input_Layer = nodes.Input(n=input_N, shape=Shape, traces=True)
out = nodes.LIFNodes(n=Output_N, refrac=0, traces=True)
network.add_layer(layer=Input_Layer, name="Input Layer")
for key_l in list(layer_list.keys()):
network.add_layer(layer=layer_list[key_l], name=str(key_l))
network.add_layer(layer=out, name="Output Layer")
if len(layer_list.keys()) == 0:
layer = nodes.LIFNodes(n=1, traces=True)
network.add_layer(layer=layer, name="mid layer")
inpt_connection = Connection(source=Input_Layer,
target=layer,
w=Weight * torch.ones(input_N))
opt_connection = Connection(source=layer,
target=out,
w=Weight * torch.ones(1))
network.add_connection(inpt_connection,
source="Input_Layer",
target="mid layer")
network.add_connection(opt_connection,
source="mid layer",
target="Output Layer")
else:
for key_ic in list(layer_list.keys()):
inpt_connection = Connection(source=Input_Layer,
target=layer_list[key_ic],
w=Weight * torch.ones(input_N))
network.add_connection(inpt_connection,
source="Input_Layer",
target=str(key_ic))
for key_op in list(layer_list.keys()):
output_connection = Connection(source=layer_list[key_op],
target=out,
w=Weight * torch.ones(1),
update_rule=MSTDP)
network.add_connection(output_connection,
source=str(key_op),
target="Output Layer")
for generating_protein in Decoded_RNA:
mid_connection = Connection(
source=layer_list[generating_protein[0]],
target=layer_list[generating_protein[1]],
w=Weight * torch.ones(1),
update_rule=MSTDP)
network.add_connection(mid_connection,
source=str(generating_protein[0]),
target=str(generating_protein[1]))
network_list.append(network)
network.save('Network/' + str(i) + '.pt')
return network_list
# Connections between layers.
inpt_middle = Connection(source=inpt, target=middle, wmax=1e-2)
middle_out = Connection(
source=middle,
target=out,
wmax=0.5,
update_rule=MSTDPET,
nu=2e-2,
norm=0.15 * middle.n,
)
# Add all layers and connections to the network.
network.add_layer(inpt, name="X")
network.add_layer(middle, name="Y")
network.add_layer(out, name="Z")
network.add_connection(inpt_middle, source="X", target="Y")
network.add_connection(middle_out, source="Y", target="Z")
# Load SpaceInvaders environment.
environment = GymEnvironment(
"SpaceInvaders-v0",
BernoulliEncoder(time=int(network.dt), dt=network.dt),
history_length=2,
delta=4,
)
environment.reset()
# Plotting configuration.
plot_config = {
"data_step": 1,
"data_length": 10,
# Spike recordings for all layers.
spikes = {}
for layer in layers:
spikes[layer] = Monitor(layers[layer], ["s"], time=plot_interval)
# 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,