def show(title, a, b, c, d):
n = Neuron(0.0, a, b, c, d)
spike_train = []
for i in range(1000):
n.current = 0.0 if i < 100 or i > 800 else 10.0
spike_train.append((1.0 * i, n.current, n.v, n.u))
print('{0:d}\t{1:f}\t{2:f}\t{3:f}'.format(i, n.current, n.v, n.u))
n.advance()
visualize.plot_spikes(spike_train, view=False, title=title)
def show(title, a, b, c, d):
n = Neuron(0.0, a, b, c, d)
spike_train = []
for i in range(1000):
n.current = 0.0 if i < 100 or i > 800 else 10.0
spike_train.append((1.0 * i, n.current, n.v, n.u))
print('{0:d}\t{1:f}\t{2:f}\t{3:f}'.format(i, n.current, n.v, n.u))
n.advance()
visualize.plot_spikes(spike_train, view=False, title=title)
def test_network():
neurons = {0: Neuron(0, 0.02, 0.2, -65.0, 8.0),
1: Neuron(0, 0.02, 0.2, -65.0, 8.0),
2: Neuron(0, 0.02, 0.2, -65.0, 8.0)}
inputs = [0, 1]
outputs = [2]
connections = [(0, 2, 0.123), (1, 2, 0.234)]
net = IzNetwork(neurons, inputs, outputs, connections)
net.set_inputs([1.0, 0.0])
net.advance()
net.advance()
def test_basic():
n = Neuron(10, 0.02, 0.2, -65.0, 8.0)
spike_train = []
for i in range(1000):
spike_train.append(n.v)
n.advance()
