| Project/Area Number |
24800013
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
OKU Makito 東京大学, 生産技術研究所, 助教 (30633565)
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| Project Period (FY) |
2012-08-31 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| 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.
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