Dynamic Learning Characteristics of Chaotic Neural Networks with Stimulus-Response Scheme

Abstract

In this paper, a stimulus-response scheme is proposed in chaotic neural networks with synaptic plasticities and the processes of dynamic learning under external stimuli are investigated. Owing to the refractoriness and the time-hysteresis, fixing abilities of patterns of stimuli become much higher than those based on Hopfield neural network and they also have sensitive dependences on the strength of stimulation. These characteristics turn out to be supported with the chaotic activity by examining the relation between the refractoriness and time-hysteresis, and the (maximum) Lyapunov exponent during the engraving of stimuli on the network. The above results indicate the importance of neural chaos when we try to realize the biologically relevant, real-time learning mechanism interactive with the outside, which is difficult for the Hopfield neural network showing poor response behaviors.