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Replica-symmetry breaking in neural networks

Author

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  • Dotsenko, V.S.
  • Tirozzi, B.

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.

Suggested Citation

  • Dotsenko, V.S. & Tirozzi, B., 1992. "Replica-symmetry breaking in neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 185(1), pages 385-394.
    • Handle: RePEc:eee:phsmap:v:185:y:1992:i:1:p:385-394
    DOI: 10.1016/0378-4371(92)90479-A
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    1. Kirkpatrick, Jeane, 1975. "Representation in the American National Conventions: the Case of 1972," British Journal of Political Science, Cambridge University Press, vol. 5(3), pages 265-322, July.
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    1. Shuai, J.W. & Chen, Z.X. & Liu, R.T. & Wu, B.X., 1995. "The Hamilton neural network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 216(1), pages 20-31.

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