Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation

F. L. Metz; W. K. Theumann

doi:10.1103/physreve.72.021908

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Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation

F. L. Metz·W. K. Theumann

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Abstract

The dynamics and the stationary states for the competition between pattern reconstruction and asymmetric sequence processing are studied here in an exactly solvable feed-forward layered neural network model of binary units and patterns near saturation. Earlier work by Coolen and Sherrington on a parallel dynamics far from saturation is extended here to account for finite stochastic noise due to a Hebbian and a sequential learning rule. Phase diagrams are obtained with stationary states and quasi-periodic non-stationary solutions. The relevant dependence of these diagrams and of the quasi-periodic solutions on the stochastic noise and on initial inputs for the overlaps is explicitly discussed.

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BibTeX RIS

F. L. Metz, W. K. Theumann. 2005-07-01. Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation. https://doi.org/10.1103/physreve.72.021908

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