Noise-driven attractor switching device

Naoki Asakawa, Yasushi Hotta, Teruo Kanki, Tomoji Kawai, and Hitoshi Tabata
Phys. Rev. E 79, 021902 – Published 2 February 2009

Abstract

Problems with artificial neural networks originate from their deterministic nature and inevitable prior learnings, resulting in inadequate adaptability against unpredictable, abrupt environmental change. Here we show that a stochastically excitable threshold unit can be utilized by these systems to partially overcome the environmental change. Using an excitable threshold system, attractors were created that represent quasiequilibrium states into which a system settles until disrupted by environmental change. Furthermore, noise-driven attractor stabilization and switching were embodied by inhibitory connections. Noise works as a power source to stabilize and switch attractors, and endows the system with hysteresis behavior that resembles that of stereopsis and binocular rivalry in the human visual cortex. A canonical model of the ring network with inhibitory connections composed of class 1 neurons also shows properties that are similar to the simple threshold system.

    • Received 6 March 2008

    DOI:https://doi.org/10.1103/PhysRevE.79.021902

    ©2009 American Physical Society

    Authors & Affiliations

    Naoki Asakawa*, Yasushi Hotta, Teruo Kanki, and Tomoji Kawai

    • Institute of Scientific and Industrial Research, Osaka University, Mihogaoka, Ibaraki, Osaka, Japan

    Hitoshi Tabata

    • Graduate School of Engineering, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan

    • *asakawa@sanken.osaka-u.ac.jp
    • kawai@sanken.osaka-u.ac.jp

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    Issue

    Vol. 79, Iss. 2 — February 2009

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