Summary

2007 International Symposium on Nonlinear Theory and its Applications

2007

Session Number:18AM1-B

Session:

Number:18AM1-B-3

An associative chaotic Neural network with Gap-Junctions

Masaharu ADACHI,  Kazuyuki AIHARA,  

pp.132-135

Publication Date:2007/9/16

Online ISSN:2188-5079

DOI:10.34385/proc.41.18AM1-B-3

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Summary:
In the present paper, we incorporate gap-junction connections into associative chaotic neural networks. The constituent neurons in the network are fully connected to other neurons in the network through synaptic weights with a delay. The neighboring neurons in the network are connected through gap-functions. The gap-junction connections are modeled by taking the difference between internal states of neurons connected through gap-junctions without delay. We investigate retrieval dynamics of the associative chaotic neural network with the gap-junction connections. As a result, the associative chaotic neural network with the gap-junction connections of medium strengh is easier to show non-periodic retrieval dynamics than the network without gap-junction connections.