A layered neural network with three-state neurons optimizing the mutual information

D. Bollé, R. Erichsen and W.K. Theumann

Physica A: Statistical Mechanics and its Applications, 2004, vol. 333, issue C, 516-528

Abstract: The time evolution of an exactly solvable layered feedforward neural network with three-state neurons and optimizing the mutual information is studied for arbitrary synaptic noise (temperature). Detailed stationary temperature-capacity and capacity–activity phase diagrams are obtained. The model exhibits pattern retrieval, pattern-fluctuation retrieval and spin-glass phases. It is found that there is an improved performance in the form of both a larger critical capacity and information content compared with three-state Ising-type layered network models. Flow diagrams reveal that saddle-point solutions associated with fluctuation overlaps slow down considerably the flow of the network states towards the stable fixed points.

Keywords: Layered neural networks; Dynamics; Mutual information (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:333:y:2004:i:c:p:516-528

DOI: 10.1016/j.physa.2003.10.033

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