A noise-driven attractor switching device

Naoki Asakawa; Yasushi Hotta; Teruo Kanki; Hitoshi Tabata; Tomoji Kawai

doi:10.1103/physreve.79.021902

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arXiv · 0901.2970

A noise-driven attractor switching device

Naoki Asakawa·Yasushi Hotta·Teruo Kanki·Hitoshi Tabata·Tomoji Kawai

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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 quasi-equilibrium 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.

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Naoki Asakawa, Yasushi Hotta, Teruo Kanki, Hitoshi Tabata, Tomoji Kawai. 2009-01-20. A noise-driven attractor switching device. https://doi.org/10.1103/physreve.79.021902

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