Spatio-temporally coding and integrating different sources of information from the view point of nonlinear systems theory

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This study aimed at understanding information integration within the brain from the viewpoint of nonlinear systems theory by describing its information coding mechanisms and nonlinear dynamics with mathematical models. We showed that integration of visual and auditory information can be explained by Bayesian estimation containing a binary parameter showing whether the two pieces of information came from the same source or not. In addition, we proposed methods for analyzing neural spike train data, and developed a method for inferring directional couplings between two regions/areas in the brain. Further, we pointed out the possibility that instability is effectively used in information processing in the brain.