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Automatic learning in chaotic neural networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84301

Watanabe,  M
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Watanabe, M., Aihara, K., & Kondo, S. (1996). Automatic learning in chaotic neural networks. Electronics and Communications in Japan III: Fundamental Electronic Science, 79(3), 87-93. doi:10.1002/ecjc.4430790309.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-EBAC-F
Zusammenfassung
A fully local algorithm which can automatically detect and learn an unknown pattern is proposed for a mutually connected recurrent neural network, and its fundamental properties are numerically analyzed. the algorithm is applied to chaotic neural networks composed of neuron models with spatiotemporal inputs and refractoriness and to conventional mutually connected neural networks. It is shown that the former could learn more patterns with greater robustness than the latter.