# Network parameters
w_self = 1.0
w_next = -1
w_rest = -3.0
w_diff = g_w_ampa * (w_self - w_next)
# Build the network
nn = BCPNNPerfect(hypercolumns,
minicolumns,
g_w_ampa=g_w_ampa,
g_w=g_w,
g_a=g_a,
tau_a=tau_a,
sigma=sigma,
G=G,
tau_z_pre_ampa=tau_z_pre_ampa,
tau_z_post_ampa=tau_z_post_ampa,
tau_p=tau_p,
g_I=g_I,
z_transfer=z_transfer,
diagonal_zero=False,
strict_maximum=strict_maximum,
perfect=perfect,
k_perfect=k_perfect,
always_learning=always_learning)
nn.g_beta = 0.0
# Build the manager
manager = NetworkManager(nn=nn, dt=dt, values_to_save=values_to_save)
w = simple_bcpnn_matrix(minicolumns, w_self, w_next, w_rest)
nn.w_ampa = w
values_to_save = ['o', 's', 'i_ampa', 'a']
# Protocol
training_time = 0.100
inter_sequence_interval = 0.0
# Recall
T_cue = 0.020
T_recall = 1.0 + T_cue
n = 1
# Neural Network
nn = BCPNNPerfect(hypercolumns, minicolumns, g_w_ampa=g_w_ampa, g_w=g_w, g_a=g_a, tau_a=tau_a, tau_m=tau_m,
sigma=sigma, G=G, tau_z_pre_ampa=tau_z_pre_ampa, tau_z_post_ampa=tau_z_post_ampa, tau_p=tau_p,
z_transfer=z_transfer, diagonal_zero=diagonal_zero, strict_maximum=strict_maximum,
perfect=perfect, k_perfect=k_perfect, always_learning=always_learning,
normalized_currents=normalized_currents)
# Build the manager
manager = NetworkManager(nn=nn, dt=dt, values_to_save=values_to_save)
# Protocol
matrix = create_orthogonal_canonical_representation(minicolumns, hypercolumns)
seq = np.copy(matrix)
seq[4] = matrix[2]
seq[5:] = matrix[4:-1]
nr = build_network_representation(seq, minicolumns, hypercolumns)
n_connections = len(seq) - 1
value = 1.0
w_rest_vector_tau_z = np.zeros_like(tau_z_vector)
successes = np.zeros_like(tau_z_vector)
for index, tau_z_pre_ampa_ in enumerate(tau_z_vector):
# Build the network
nn = BCPNNPerfect(hypercolumns,
minicolumns,
g_w_ampa=g_w_ampa,
g_w=g_w,
g_a=g_a,
tau_a=tau_a,
tau_m=tau_m,
sigma=sigma,
G=G,
tau_z_pre_ampa=tau_z_pre_ampa_,
tau_z_post_ampa=tau_z_pre_ampa_,
tau_p=tau_p,
z_transfer=z_transfer,
diagonal_zero=diagonal_zero,
strict_maximum=strict_maximum,
perfect=perfect,
k_perfect=k_perfect,
always_learning=always_learning)
# Build the manager
manager = NetworkManager(nn=nn, dt=dt, values_to_save=values_to_save)
# Build the protocol for training
protocol = Protocol()
patterns_indexes = [i for i in range(n_patterns)]
values_to_save = ['o', 's', 'z_pre', 'z_post', 'a', 'i_ampa', 'i_nmda']
# Protocol
training_time = 0.100
inter_sequence_interval = 0
inter_pulse_interval = 0.0
epochs = 1
# Build the network
nn = BCPNNPerfect(hypercolumns,
minicolumns,
g_w_ampa=g_w_ampa,
g_w=g_w,
g_a=g_a,
tau_a=tau_a,
sigma=sigma,
G=G,
tau_z_pre=tau_z_pre,
z_transfer=False,
diagonal_zero=False,
strict_maximum=True,
perfect=True)
# Build the manager
manager = NetworkManager(nn=nn, dt=dt, values_to_save=values_to_save)
nn.z_pre = np.zeros(nn.n_units)
# Build the protocol for training
protocol = Protocol()
patterns_indexes = [i for i in range(n_patterns)]
protocol.simple_protocol(patterns_indexes,
training_time=training_time,
# Patterns parameters
hypercolumns = 1
minicolumns = 10
n_patterns = 10
# Manager properties
dt = 0.001
values_to_save = ['o', 's', 'a', 'i_ampa']
nn = BCPNNPerfect(hypercolumns,
minicolumns,
g_w_ampa=g_w_ampa,
g_w=g_w,
g_a=g_a,
tau_a=tau_a,
sigma=sigma,
G=G,
z_transfer=False,
diagonal_zero=False,
strict_maximum=strict_maximum,
perfect=perfect)
# Build the manager
manager = NetworkManager(nn=nn, dt=dt, values_to_save=values_to_save)
w = simple_bcpnn_matrix(minicolumns, w_self, w_next, w_rest)
nn.w_ampa = w
# Recall
T_recall = 2.2
T_cue = 0.080
sequences = [[i for i in range(n_patterns)]]