Response Properties to Inputs of Memory Pattern Fragments in Three Types of Chaotic Neural Network Models

In this paper, we investigate response properties to inputs of memory pattern fragments in chaotic wandering states among three types of chaotic neural network (CNN) models, related with the instability of their orbits. From the computer experiments, Aihara model shows the highest success ratio and the shortest steps for all the memory pattern fragments. On the other hand, Nara & Davis model and Kuroiwa & Nara model show quite higher success ratio and shorter averaged steps than random search. Thus, choas in the three model is practical in the memory pattern search.