2022 Fiscal Year Final Research Report
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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| Keywords | tropical geometry / information geometry / deep learning / default mode networks / neural coding |
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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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| Free Research Field |
Computational Neuroscience
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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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