A model for visual selective attention by two-layered neural network with FitzHugh-Nagumo neurons

We propose a mathematical model of visual selective attention using a two-layered neural network with spiking neurons described by FitzHugh-Nagumo equation, based on an assumption proposed by Desimone and Duncan. The neural network consists of a layer of hippocampal formation and that of visual cortex. We solve by numerical calculations a set of first-order ordinary differential equations, which describe a time-evolution of each neuron, in order to clarify an attention state, a non-attention state and an attention shift. We find that synchronous phenomena occur not only for a frequency but also for a firing time between the neurons in the hippocampal formation and those in a part of the visual cortex. 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.