Open Access
2012 A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory
Xiaofang Hu, Shukai Duan, Lidan Wang
Abstr. Appl. Anal. 2012(SI10): 1-19 (2012). DOI: 10.1155/2012/405739


Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1) nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2) it can separate stored patterns from superimposed input; (3) it can deal with one-to-many associative memory; (4) it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.


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Xiaofang Hu. Shukai Duan. Lidan Wang. "A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory." Abstr. Appl. Anal. 2012 (SI10) 1 - 19, 2012.


Published: 2012
First available in Project Euclid: 5 April 2013

zbMATH: 1257.68129
MathSciNet: MR2999904
Digital Object Identifier: 10.1155/2012/405739

Rights: Copyright © 2012 Hindawi

Vol.2012 • No. SI10 • 2012
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