Associative dynamics in a chaotic neural network
Neural Networks
Artificial Life and Robotics
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A chaotic neural network proposed (CNN) by Aihara et al. is able to recollect stored patterns dynamically. But there are difficult cases such as its long time processing of association, and difficult to recall a specific stored pattern during the dynamical associations. We have proposed to find the optimal parameters using meta-heuristics methods to improve association performance, for example, the shorter recalling time and higher recollection rates of stored patterns in our previous works. However, the relationship between the different values of parameters of chaotic neurons and the association performance of CNN was not investigated clearly. In this paper, we propose a method to analyze the spatiotemporal changes of internal states in CNN and, by the method, analyze how the change of values of internal parameters of chaotic neurons affects the characteristics of chaotic neurons when multiple patterns are stored in the CNN. Quantile---Quantile plot, least square approximation, hierarchical clustering, and Hilbert transform are used to investigate the similarity of internal states of chaotic neurons, and to classify the neurons. Simulation results showed that how different values of an internal parameter yielded different behaviors of chaotic neurons and it suggests the optimal parameter which generates higher association performance may concern with the stored patterns of the CNN.