Dynamic depression control of chaotic neural networks for associative memory

Abstract

The chaotic neural network constructed with chaotic neurons presents complex dynamics and has potential application in the associative dynamics and information processing. However, the states of the chaotic neural network wander around all the stored patterns and cannot be stabilized to one of the stored patterns or a periodic orbit because of the chaotic characteristic of the network, which hampering the application of the chaotic associative dynamics of chaotic neural network to information processing. In this work, a dynamic depression control method imposed on the internal state of neurons for chaotic neural networks is proposed. In this way, the decay parameters and the scaling parameter for the refractoriness are time varying determined by the internal state of neurons. Ascribing to dynamic depression control, chaos is controlled in a self-adaptive manner and no target needs to be specified in advance. Furthermore, the theoretic analysis of dynamic depression control is presented. The numerical simulation proves that the chaos in the chaotic neural network can be controlled with the dynamic depression control, and the neural network can be stabilized to a stored pattern if the control strength parameter is chosen suitable.