Synchronous Phenomena for Two-Layered Neural Network with Chaotic Neurons
We propose a mathematical model of visual selective attention using a two-layered neural network, based on an assumption proposed by Desimone and Duncan. We use a spiking neuron model proposed by Hayashi and Ishizuka, which generates periodic spikes, quasiperiodic spikes and chaotic spikes. The neural network consists of a layer of hippocampal formation and that of visual cortex. In order to clarify an attention shift, we solve numerically a set of the first-order ordinary differential equations, which describe a time-evolution of neurons. The visual selective attention is considered as the synchronous phenomena between the firing times of the neurons in the hippocampal formation and those in a part of the visual cortex in the present model.
KeywordsVisual Cortex Lyapunov Exponent External Input Hippocampal Formation Attention Shift
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