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