Mathematical Modeling of Attention and Consciousness by Using Layered Neural Networks

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Studies on higher function in brain such as memory, learning, attention and consciousness by using artificial neural networks are so important for understanding and clarification of information processing ability in brains of animals of higher orders. Those research subjects are one of ultimate themes of mankind. Our research group has been tried to give a step for mathematical modeling for those higher function in the brain including visual attention and so on. Some of the obtained results in the present project are as follows:(1) We investigated visual selective attention by constructing of two-layered neural network model with spiking neurons described by FitHugh-Nagumo equation, based on a hypothesis given by Desimone and Duncan. Namely the two layers consist of a layer of hippocampal formation and that of visual cortex. We found that the visual selective attention is the synchronous phenomena for a frequency and also firing time between neurons on those layers (See Ref. (10).)(2) We investigated visual selective attention by using Hodgkin-Huxley neurons for the two-layered neural network model. (See Ref.(11).)(3) Attention and consciousness are now under investigation by constructing a mathematical model using the hypothesis by Crick and the one by Desimone and Duncan. (In preparation.)(4) We investigated the memory recall in dynamical neural network models. For example, we succeeded in the autoassociative memory recall and also the heteroassociative memory recall by using the proposed model. (See Refs.(5), (6) and (7).)(5) We investigated the emergence of the theta and the gamma oscillation by constructing a two-layered neural network model with FitzHugh-Nagumo neurons. (See Ref. (8).)(6) fe investigated working memory, learning, MST neurons for optical flows and so on, (See Refs.(1)-(3) and (9).)