Chaos in transiently chaotic neural networks

Abstract

It was theoretically proved that one-dimensional transiently chaotic neural networks have chaotic structure in sense of Li-Yorke theorem with some given assumptions using that no division implies chaos. In particular, it is further derived sufficient conditions for the existence of chaos in sense of Li-Yorke theorem in chaotic neural network, which leads to the fact that Aihara has demonstrated by numerical method. Finally, an example and numerical simulation are shown to illustrate and reinforce the previous theory.