Recall of Sequences of Items by a Neural Network

A network architecture of the forward type but with additional ‘memory’ units that store the hidden units activation at time 1 and re-input this activation to the hidden units at time 2, is used to train a network to free recall sequences of items. The network's performance exhibits some features that are also observed in humans, such as decreasing recall with increasing sequence length and better recall of the first and the last items compared with middle items. An analysis of the network's behavior during sequence presentation can explain these results.