Weight Patterns

save_patterns(h5_file, items, **kwargs)

Write patterns and similarity matrices to hdf5.

load_patterns(h5_file[, features])

Load weights from an hdf5 file.

prepare_patterns(patterns, weights)

Scale and concatenate item patterns and connections.

unpack_weights(weight_template, weight_param)

Apply parameter values to a weight template.

Network Initialization

Network(f_segment, c_segment)

Representation of interacting item and context layers.

Accessing and Setting Values


Copy the network to a new network object.


Reset network weights and activations to zero.

Network.get_sublayer(layer, sublayer)

Get indices for a sublayer.

Network.get_sublayers(layer, sublayers)

Get an array of indices for multiple sublayers.

Network.get_segment(layer, sublayer, segment)

Get indices for a segment.

Network.get_unit(layer, sublayer, segment, unit)

Get indices for a unit.

Network.add_pre_weights(connect, f_segment, …)

Add pre-experimental weights to a network.


Network.update(item, sublayers)

Update context completely with input from the item layer.

Network.integrate(item, sublayers, B)

Integrate input from the item layer into context.

Network.present(item, sublayers, B[, Lfc, Lcf])

Present an item and learn context-item associations.

Network.learn(connect, item, sublayers, L)

Learn an association between the item and context layers.

Simulating Tasks, item_list, sublayers, …)

Study a list of items.

Network.p_recall(segment, recalls, …[, amin])

Calculate the probability of a specific recall sequence.

Network.generate_recall(segment, sublayers, …)

Generate a sequence of simulated free recall events.

Network.generate_recall_lba(segment, …)

Generate timed free recall using the LBA model.

Recording and Visualization

Network.record_study(segment, item_list, …)

Network.record_recall(segment, recalls, …)


Plot the current state of the network.