Noise-driven attractor switching device

• Published in: Physical review. E, Statistical, nonlinear, and soft matter physics Vol. 79; no. 2 Pt 1; p. 021902
• Main Authors: Asakawa, Naoki, Hotta, Yasushi, Kanki, Teruo, Kawai, Tomoji, Tabata, Hitoshi
• Format: Journal Article
• Language: English
• Published: United States 01.02.2009
• Subjects: Computer Simulation; Models, Statistical; Neural Networks (Computer); Signal Processing, Computer-Assisted - instrumentation; Stochastic Processes
• ISSN: 1539-3755
• DOI: 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 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.