Mutual information and self-control of a fully-connected low-activity neural network

D Bollé and D.Dominguez Carreta

Physica A: Statistical Mechanics and its Applications, 2000, vol. 286, issue 3, 401-416

Abstract: A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

Keywords: Self-control dynamics; Mutual information; Fully-connected network; Storage capacity; Basin of attraction (search for similar items in EconPapers)
Date: 2000
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:286:y:2000:i:3:p:401-416

DOI: 10.1016/S0378-4371(00)00308-3

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