title: Dynamical foundations of the neural circuit for bayesian decision making.
abstract: On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.
authors.affiliation: RIKEN Brain Science Institute, Wako, Japan. kmorita@m.u-tokyo.ac.jp
authors.forename: Kenji
authors.initials: K
authors.lastname: Morita
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D017711:Nonlinear Dynamics