Unsupervised context-based learning of multiple temporal sequences

Abstract

A self-organizing neural network is proposed to handle multiple temporal sequences with states in common. The proposed network combines context-based competitive learning with time-delayed Hebbian learning to encode spatial features and temporal order of sequence items. A responsibility function to avoid catastrophic forgetting, and a redundancy mechanism to provide noise and fault tolerance increase the reliability of the model. States shared by different sequences are encoded by a single neuron, whereas context information indicates the correct sequence to be recalled in the case of ambiguity. Simulations with trajectories of a PUMA 560 robot are performed to test the network accuracy, robustness to noise and tolerance to faults.