Coexistence of exponentially many chaotic spin-glass attractors

Y. Peleg, M. Zigzag, W. Kinzel, I. Kanter

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2 Scopus citations


A chaotic network of size N with delayed interactions which resembles a pseudoinverse associative memory neural network is investigated. For a load α=P/N<1, where P stands for the number of stored patterns, the chaotic network functions as an associative memory of 2P attractors with macroscopic basin of attractions which decrease with α. At finite α, a chaotic spin-glass phase exists, where the number of distinct chaotic attractors scales exponentially with N. Each attractor is characterized by a coexistence of chaotic behavior and freezing of each one of the N chaotic units or freezing with respect to the P patterns. Results are supported by large scale simulations of networks composed of Bernoulli map units and Mackey-Glass time delay differential equations.

Original languageEnglish
Article number066204
JournalPhysical Review E
Issue number6
StatePublished - 12 Dec 2011


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