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IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications
Article . 1993 . Peer-reviewed
License: IEEE Copyright
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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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The CNN paradigm

The \(CNN\) paradigm
descriptionPublicationkeyboard_double_arrow_right Article 01 Mar 1993Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, volume 40, pages 147-156 (issn: 1057-7122, Copyright policy )
Authors: Leon O. Chua; Tamás Roska;

doi: 10.1109/81.222795

The CNN paradigm

Abstract

A concise tutorial description of the cellular neural network (CNN) paradigm is given, along with a precise taxonomy. The CNN is defined, and the canonical equations are described. The importance of many independent input signal arrays, adaptive templates, and the multilayer capability is emphasized and motivated by examples. It is shown how simply a wave-type partial differential equation can be generated. >

Related Organizations
Keywords

Switching theory, application of Boolean algebra; Boolean functions, Neural networks for/in biological studies, artificial life and related topics

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1K
Top 0.1%
Top 0.1%
Top 1%
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