C2 = Connection(L1,
L1,
allBut1(L1.num_neurons) * inh,
params,
rule = "static",
wmin = inh,
wmax = 0)
network = Network([Input, L1], [C1, C2])
''' Import and load saved weights '''
weight_file = open('weights.pickle', 'rb')
weights = pickle.load(weight_file)
weight_file.close()
network.set_weights(weights)
''' Record spiking activity of Layer 1 while showing it a bunch of images '''
print("Recording Spikes")
num_images = 1
time_step = 50
spikes = []
for i in tqdm(range(num_images)):
network.populations[0].set_input(x_train[i])
spikes.append(network.run(time_step, learning=False, record_spikes=True, pop_index=1).get("spikes"))
network.rest()
spikes = np.reshape(spikes, (num_neurons, time_step*num_images))
''' Calculate correlation matrix from spiking activity '''
corr = np.zeros((len(spikes), len(spikes)))
