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

Author

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

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.

Suggested Citation

  • Bollé, D. & Erichsen, R. & Theumann, W.K., 2004. "A layered neural network with three-state neurons optimizing the mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 516-528.
    • Handle: RePEc:eee:phsmap:v:333:y:2004:i:c:p:516-528
    DOI: 10.1016/j.physa.2003.10.033
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