  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.
