Chicago Journal of Theoretical Computer Science

Volume 1999

Article 6

Published by MIT Press. Copyright 1999 Massachusetts Institute of Technology.

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Hopfield Neural Networks and Self-Stabilization

Arun Jagota (University of California, Santa Cruz)
6 August 1999

This paper studies Hopfield neural networks from the perspective of self-stabilizing distributed computation. Known self-stabilization results on Hopfield networks are surveyed. Key ingredients of the proofs are given. Novel applications of self-stabilization---associative memories and optimization---arising from the context of neural networks are discussed. Two new results at the intersection of Hopfield nets and of distributed systems are obtained: One involves convergence under a fine-grained implementation; the other is on perturbation analysis. Some possibilities for further research at the intersection of these two fields are discussed.

DOI: 10.4086/cjtcs.1999.006
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Last modified: Sat Mar 20 09:32:31 CST 1999