@inbook {IOPORT.05845189,
    author       = {Kawamura, Tetsuo and Horio, Yoshihiko and Hasegawa, Mikio},
    title        = {Mutual information analyses of chaotic neurodynamics driven by neuron selection methods in synchronous exponential chaotic tabu search for quadratic assignment problems.},
    year         = {2010},
    booktitle    = {Neural information processing. Theory and algorithms. 17th international conference, ICONIP 2010, Sydney, Australia, November 22--25, 2010. Proceedings, Part I},
    isbn         = {978-3-642-17536-7},
    pages        = {49-57},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-17537-4_7},
    abstract     = {Summary: The exponentially decaying tabu search, which exhibits high performance in solving quadratic assignment problems (QAPs), has been implemented on a neural network with chaotic neurodynamics. To exploit the inherent parallel processing capability of analog hardware systems, a synchronous updating scheme, in which all neurons in the network are updated simultaneously, has also been proposed. However, several neurons may fire simultaneously with the synchronous updating. As a result, we cannot determine only one candidate for the 2-opt exchange from among the many fired neurons. To solve this problem, several neuron selection methods, which select a specific neuron from among the fired neurons, have been devised. These neuron selection methods improved the performance of the synchronous updating scheme; however, the dynamics of the chaotic neural network driven by these heuristic algorithms cannot be intuitively understood. In this paper, we analyze the dynamics of a chaotic neural network driven by the neuron selection methods by considering the spatial and temporal mutual information.},
    identifier   = {05845189},
}