title: Noise-driven attractor switching device.
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.
authors.affiliation: Institute of Scientific and Industrial Research, Osaka University, Mihogaoka, Ibaraki, Osaka, Japan. asakawa@sanken.osaka-u.ac.jp
authors.forename: Naoki
authors.initials: N
authors.lastname: Asakawa
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Hotta
authors.forename: Teruo
authors.initials: T
authors.lastname: Kanki
authors.forename: Tomoji
authors.initials: T
authors.lastname: Kawai
authors.forename: Hitoshi
authors.initials: H
authors.lastname: Tabata
mesh_terms: D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013269:Stochastic Processes