Computational study of neural information processing for perceptual constancy under changing environments
| Project/Area Number |
18K11485
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61040:Soft computing-related
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| Research Institution | Kwansei Gakuin University |
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Principal Investigator |
Miura Keiji 関西学院大学, 生命環境学部, 教授 (60520096)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | tropical geometry / information geometry / deep learning / default mode networks / neural coding / Tropical geometry / topology / visual cortex / theoretical neuroscience / noise correlations / nonstationarity / dopaminergic neurons / striatum / substantia nigra / 計算論的神経科学 / 情報幾何学 / default mode network |
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| Outline of Final Research Achievements |
We investigated the homeostasis of neural coding under changing environments by using information geometry and tropical geometry. Especially, we developed a new measure of correlations which is orthogonal to the background baselines of two time series. Also, we extended various machine learning methods to the tropical geometric setting in order to treat the time series with trends. We published 13 research articles.
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| Academic Significance and Societal Importance of the Research Achievements |
脳の研究においては，ある神経細胞がどんな感覚刺激に応答するのかを調べるのが慣例であるが，近年は何も刺激が無い時の活動も注目されつつある.そして意外なことに，この背景活動は大きく変動する一方，刺激に対する応答は実は相対的に小さい事が報告された.本課題では，はたして脳は，背景活動が大変動する中，どのようにして感覚刺激を認識できるのか，の解明を目指した.背景活動を考慮した新規脳情報処理モデルの導出は，より精度の高い脳信号解読を可能とし，深層学習の設計指針を与え，脳疾患の病因解明にも繋がりうると期待される．
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Report
(6 results)
Research Products
(49 results)
