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Finite Size Effects in Separable Recurrent Neural Networks

A. Castellanos·A. C. C. Coolen·L. Viana
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Abstract

We perform a systematic analytical study of finite size effects in separable recurrent neural network models with sequential dynamics, away from saturation. We find two types of finite size effects: thermal fluctuations, and disorder-induced `frozen' corrections to the mean-field laws. The finite size effects are described by equations that correspond to a time-dependent Ornstein-Uhlenbeck process. We show how the theory can be used to understand and quantify various finite size phenomena in recurrent neural networks, with and without detailed balance.

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A. Castellanos, A. C. C. Coolen, L. Viana. 1998-03-31. Finite Size Effects in Separable Recurrent Neural Networks. https://doi.org/10.1088/0305-4470%2F31%2F31%2F009

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