Associative memory using nonlinear line attractor network for multi-valued pattern association

Authors:
Ming-Jung Seow;Vijayan K. Asari
Affiliations:
Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

A method to embed P multi-valued patterns xs∈ℝN into a memory of a recurrent neural network is introduced. The method represents memory as a nonlinear line of attraction as opposed to the conventional model that stores memory in attractive fixed points at discrete locations in the state space. The activation function of the network is defined by the statistical characteristics of the training data. The stability of the proposed nonlinear line attractor network is investigated by mathematical analysis and extensive computer simulation. The performance of the network is benchmarked by reconstructing noisy gray-scale images.