Freezing transitions in neural networks

K.Y.M. Wong

Physica A: Statistical Mechanics and its Applications, 1994, vol. 205, issue 1, 399-406

Abstract: I consider neural networks as feedback dynamical systems for retrieving information, which in the retrieval phase is driven to an attractor correlated with a stored pattern. Dynamics in this attractor may be described by considering the activity distribution. This enables us to determine the degree of chaoticity of the network dynamics. Consequently I am able to demonstrate that as more patterns are stored the system becomes more chaotic, and undergoes a transition for a partially frozen phase to an unforzen phase. Improvement in retrieval using a freezing procedure is also discussed.

Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:205:y:1994:i:1:p:399-406

DOI: 10.1016/0378-4371(94)90518-5

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