Suppressing chaos with hysteresis in a higher order neural network

Artificial neural networks (ANNs) attempt to mimic various features of a most powerful computational system-the human brain. Since ANNs consist of a large number of parallel arrays of simple processing elements (neurons), they are naturally suited for today's fast-developing VLSI technology. For instance, a programmable analog neural oscillator with hysteresis appropriate for monolithic integrated circuits. Dynamic systems have many applications; however, stability is often desired. We show analytically that hysteresis at the single neuron level can provide a simple means to preserve stability in an ANN even when the nature of the system is chaotic.