2007 International Symposium on Nonlinear Theory and its Applications
An associative chaotic Neural network with Gap-Junctions
Masaharu ADACHI, Kazuyuki AIHARA,
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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.