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  Automatic learning in chaotic neural networks

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-EBAC-F Version Permalink: http://hdl.handle.net/21.11116/0000-0005-E729-9
Genre: Journal Article

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Watanabe, M1, Author              
Aihara, K, Author
Kondo, S, Author
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1External Organizations, ou_persistent22              

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 Abstract: 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.

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 Dates: 1996-03
 Publication Status: Published in print
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 Identifiers: DOI: 10.1002/ecjc.4430790309
BibTex Citekey: 5803
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Title: Electronics and Communications in Japan III: Fundamental Electronic Science
Source Genre: Journal
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Pages: - Volume / Issue: 79 (3) Sequence Number: - Start / End Page: 87 - 93 Identifier: -