Storage and Recall of Complex Temporal Sequences through a Contextually Guided Self-Organizing Neural Network

A self-organizing neural network for learning and recall of complex temporal sequences is proposed. We consider a single open or closed sequence with repeated items, or several sequences with a common state. Both cases give rise to ambiguities during recall of such sequences, which is resolved through context input units. Competitive weights encode spatial features of the input sequence, while the temporal order is learned by lateral weights through a time-delayed Hebbian learning rule. Repeated or shared items are stored as a single copy resulting in an efficient memory use. In addition, redundancy in item representation improves the network robustness to noise and faults. The model operates by recalling the next state of the learned sequences and is able to solve potential ambiguities. The model is simulated with binary and analog sequences and its functioning is compared to other neural networks models.