2013 Fiscal Year Final Research Report

Basic research on brain-inspired information processing systems with integrated computation for recognition and decision making

Research Project

Project/Area Number	24800013
Research Category	Grant-in-Aid for Research Activity Start-up
Allocation Type	Single-year Grants
Research Field	Bioinformatics/Life informatics
Research Institution	The University of Tokyo
Principal Investigator	OKU Makito 東京大学, 生産技術研究所, 助教 (30633565)
Project Period (FY)	2012-08-31 – 2014-03-31
Keywords	脳型情報処理 / 確率推論 / ベイズの定理 / ダイナミックベイジアンネットワーク / 意思決定 / 近似解法 / マルコフ連鎖モンテカルロ法 / 部分観測マルコフ決定過程
Research Abstract	This research aimed for the development of a new computational theory of the brain that integrates two types of brain's functionality, that is, recognition (input-related computation) and decision making (output-related computation). We also tried to establish a fundamental mathematical model based on the theory that may contribute to the realization of highly intelligent information processing systems like the brain. Specifically, we have modeled the computation associated with recognition and decision making as probabilistic inference on a dynamic Bayesian network model in a unified manner. We have also derived the exact solution of the probabilistic inference problem and proposed an approximation method for it.