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