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A New Bi-directional Associative Memory

  • Roberto A. Vázquez
  • Humberto Sossa
  • Beatriz A. Garro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

Hebbian hetero-associative learning is inherently asymmetric. Storing a forward association from pattern A to pattern B enables the recalling of pattern B given pattern A. This, in general, does not allow the recalling of pattern A given pattern B. The forward association between A and B will tend to be stronger than the backward association between B and A. In this paper it is described how the dynamical associative model proposed in [10] can be extended to create a bi-directional associative memory where forward association between A and B is equal to backward association between B and A. This implies that storing a forward association, from pattern A to pattern B, would enable the recalling of pattern B given pattern A and the recalling of pattern A given pattern B. We give some formal results that support the functioning of the proposal, and provide some examples were the proposal finds application.

Keywords

Input Pattern Associative Memory Output Pattern Distorted Version Associative Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roberto A. Vázquez
    • 1
  • Humberto Sossa
    • 1
  • Beatriz A. Garro
    • 1
  1. 1.Centro de Investigación en Computación – IPNCiudad de MéxicoMéxico

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