Controlling a chaotic neural network for information processing

Authors:
Yang Li;Ping Zhu;Xiaoping Xie;Hongping Chen;Kazuyuki Aihara;Guoguang He
Affiliations:
Department of Physics, Zhejiang University, Hangzhou 310027, China and Key Laboratory of Infrared Imaging Materials and Detectors, Shanghai Institute of Technical Physics, Chinese Academy of Scien ...;Department of Physics, Zhejiang University, Hangzhou 310027, China;Department of Physics, Zhejiang University, Hangzhou 310027, China;Department of Physics, Zhejiang University, Hangzhou 310027, China;Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan;Department of Physics, Zhejiang University, Hangzhou 310027, China
Venue:
Neurocomputing
Year:
2013

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Abstract

A dynamic phase-space constraint method is proposed to control complex chaotic dynamics in a chaotic neural network (CNN), by limiting refractoriness internal states with a time-varying threshold. The limiting threshold evolves according to a control signal derived from the feedback internal states of the network. Simulation results reveal that the CNN under control exhibits multiphase behavior in the control parameter space. With proper parameter values, the controlled CNN converges to a periodic orbit which includes a stored pattern that has the smallest Hamming distance to its initial state. The properties of the controlled CNN can be used for information processing such as memory retrieval and pattern recognition.