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)
# Build the manager
manager = NetworkManager(nn=nn,
dt=dt,
values_to_save=values_to_save)
nn.w_ampa = w
# Recall
T_recall = 3.0
T_cue = 0.100
sequences = [[i for i in range(n_patterns)]]
n = 1
aux = calculate_recall_time_quantities(manager, T_recall,
T_cue, n, sequences)
total_sequence_time, mean, std, success, timings = aux
success_vector[index_sigma, trial] = success
persistent_time_vector[index_sigma, trial] = mean
successes_list.append(success_vector)
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
# Recall
T_recall = 0.450
T_cue = 0.050
sequences = [[i for i in range(n_patterns)]]
n = 1
aux = calculate_recall_time_quantities(manager, T_recall, T_cue, n, sequences)
total_sequence_time, mean, std, success, timings = aux
i_ampa = manager.history['i_ampa']
a = manager.history['a']
time = np.linspace(0, manager.T_total, a.shape[0])
##########
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)
# Build the protocol for training
nn.w_ampa = w_timed
# Recall
patterns_indexes = [i for i in range(n_patterns)]
sequences = [patterns_indexes]
# manager.run_network_recall(T_recall=1.0, T_cue=0.100, I_cue=0, reset=True, empty_history=True)
aux = calculate_recall_time_quantities(manager, T_recall, T_cue, n,
sequences)
total_sequence_time, mean, std, success, timings = aux
w_self, w_next, w_rest = get_weights(manager,
from_pattern,
to_pattern,
mean=False)
success_vector[index] = success
persistence_time_vector[index] = mean