Context in temporal sequence processing:: A self-organizing approach and its application to robotics

A self-organizing neural network for learning and recall of complex temporal sequences is developed and applied to robot trajectory planning. We consider trajectories with both repeated and shared states. Both cases give rise to ambiguities during reproduction of stored trajectories which are resolved via temporal context information. Feedforward weights encode spatial features of the input trajectories, while the temporal order is learned by lateral weights through a time-delayed Hebbian learning rule. After training is completed, the network model operates in an anticipative fashion by always recalling the successor of the current input state. Redundancy in sequence representation improves the robustness of the network to noise and faults. The network uses memory resources efficiently by reusing neurons that have previously stored repeated/shared states. Simulations have been carried out to evaluate the performance of the network in terms of trajectory reproduction, convergence time and memory usage, tolerance to fault and noise, and sensitivity to trajectory sampling rate. The results show that the network model is fast, accurate, and robust. Its performance is discussed in comparison with other neural-networs models.