Chaos in random neural networks

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.

Authors: H. Sompolinsky, A. Crisanti, and H. J. Sommers
Year of publication: 1988
Journal: Phys. Rev. Lett. 61, 259 – Published 18 July 1988

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“Working memory”