Replica-symmetry breaking in neural networks

V.S. Dotsenko and B. Tirozzi

Physica A: Statistical Mechanics and its Applications, 1992, vol. 185, issue 1, 385-394

Abstract: Replica-symmetry breaking is studied in fully connected neural networks with modified pseudo-inverse interactions. The interaction matrix has an intermediate form between the Hebb learning rule and the pseudo-inverse one. At low temperature there is a region of parameters where the replica-symmetric solution is stable while its entropy is negative. It indicates the existence of the alternative solution in which the replica symmetry is broken. A one-stop replica-symmetry breaking solution is found and its properties are analyzed.

Date: 1992
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:185:y:1992:i:1:p:385-394

DOI: 10.1016/0378-4371(92)90479-A

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