Investigation and modeling of neural mechanism of context prediction
| Project/Area Number |
26730124
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
FUNAMIZU Akihiro 沖縄科学技術大学院大学, 神経計算ユニット, 研究員 (20724397)
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| Research Collaborator |
KUHN Bernd 沖縄科学技術大学院大学, 光学ニューロイメージングユニット, 准教授 (90599557)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | ベイズ推定 / 大脳新皮質 / 二光子顕微鏡 / 頭頂葉 / 強化学習 / 神経科学 / 脳科学 / 計算論 / 皮質 |
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| Outline of Final Research Achievements |
This study proposed a hypothesis that neocortex employs Bayesian inference to predict current context. Bayesian inference is widely used in robotics and information science for context prediction. This hypothesis was published as a review paper. Neocortex has a six-layered column structure. Our Bayesian hypothesis proposed that cortical layers 1, 2/3, and 5 implement prior, likelihood, and posterior in the Bayesian inference, respectively. Top-down signals from other cortical areas or thalamus send prediction of future state (state transition), while bottom-up signals send sensory inputs to a cortical column. We tested our Bayesian-cortex hypothesis with mice using a behavioral training and two-photon microscopy which imaged population neuronal activity of posterior parietal cortex.
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Report
(3 results)
Research Products
(9 results)
