A noise-driven attractor switching device

Details A noise-driven attractor switching device A noise-driven attractor switching device

2009-01-20

arxiv.org/abs/0901.2970v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

9 pages, 2 tables, and 6 figures. will appear in Phy.Rev.E, vol.79, issue 1

Scientific paper

10.1103/PhysRevE.79.021902

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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