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. © 1988 The American Physical Society.
CHAOS IN RANDOM NEURAL NETWORKS / H., Sompolinsky; Crisanti, Andrea; H. J., Sommers. - In: PHYSICAL REVIEW LETTERS. - ISSN 0031-9007. - 61:3(1988), pp. 259-262. [10.1103/physrevlett.61.259]
CHAOS IN RANDOM NEURAL NETWORKS
CRISANTI, Andrea;
1988
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. © 1988 The American Physical Society.File allegati a questo prodotto
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