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Freezing transitions in neural networks

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  • Wong, K.Y.M.

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.

Suggested Citation

  • Wong, K.Y.M., 1994. "Freezing transitions in neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 205(1), pages 399-406.
    • Handle: RePEc:eee:phsmap:v:205:y:1994:i:1:p:399-406
    DOI: 10.1016/0378-4371(94)90518-5
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