Chaos in random neural networks

H. Sompolinsky; A. Crisanti; H. J. Sommers

doi:10.1103/PhysRevLett.61.259

Chaos in random neural networks

H. Sompolinsky^*
, A. Crisanti
, H. J. Sommers

^*Corresponding author for this work

Racah Institute of Physics

Research output: Contribution to journal › Article › peer-review

791 Scopus citations

Abstract

A continuous-time dynamic model of a network of N nonlinear elements interacting via random asymmetric couplings is studied. A self-consistent mean-field theory, exact in the N limit, predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter. The autocorrelations of the chaotic flow as well as the maximal Lyapunov exponent are calculated.

Original language	English
Pages (from-to)	259-262
Number of pages	4
Journal	Physical Review Letters
Volume	61
Issue number	3
DOIs	https://doi.org/10.1103/PhysRevLett.61.259
State	Published - 1988

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abstract = "A continuous-time dynamic model of a network of N nonlinear elements interacting via random asymmetric couplings is studied. A self-consistent mean-field theory, exact in the N limit, predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter. The autocorrelations of the chaotic flow as well as the maximal Lyapunov exponent are calculated.",

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Chaos in random neural networks

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