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Bifurcation analysis in an associative memory model

2003/09/30 by Masaki Kawamura, Ryuji Tokunaga, Masato Okada · 1 citation
Computer Science · Neuroscience · Physics and Astronomy · #Neural Networks and Applications #Neural dynamics and brain function #Nonlinear Dynamics and Pattern Formation #cond-mat.dis-nn

paper · pdf · doi:10.1103/physreve.70.046210

published as Phys. Rev. E 70, 046210 (2004) · 19 pages

arxiv created 2004/06/28 · openalex publication_date 2004/10/22 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We previously reported the chaos induced by the frustration of interaction in a nonmonotonic sequential associative memory model, and showed the chaotic behaviors at absolute zero. We have now analyzed bifurcation in a stochastic system, namely, a finite-temperature model of the nonmonotonic sequential associative memory model. We derived order-parameter equations from the stochastic microscopic equations. Two-parameter bifurcation diagrams obtained from those equations show the coexistence of attractors, which do not appear at absolute zero, and the disappearance of chaos due to the temperature effect.

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