2020 Fiscal Year Annual Research Report
Learning perceptual representations in biological and artificial neural networks
Publicly Offered Research
| Project Area | Correspondence and Fusion of Artificial Intelligence and Brain Science |
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| Project/Area Number |
19H04995
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
ベヌッチ アンドレア 国立研究開発法人理化学研究所, 脳神経科学研究センター, チームリーダー (50722352)
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| Project Period (FY) |
2019-06-28 – 2021-03-31
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| Keywords | visual psychophysics, / optical imaging / optogenetics / computational modeling / visual cortex |
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| Outline of Annual Research Achievements |
During FY2022, our research focused on sensory processing and sensory-based decision making, encompassing various experimental and theoretical investigations. We concluded three key studies in this period. Dr. Orlandi's study employed locaNMF tensor decomposition to extract choice signals from dorsal-parietal cortical networks during a decision-making task. This investigation revealed pervasive choice signals in these networks, reflecting top-down signals for inference in sensory and sensory-to-decision processes. The study was published in Nature Communications. Dr. Benucci conducted a separate study investigating the impact of motor-related signals in the visual cortex on perceptual stabilization during self-generated movements. Using convolutional neural networks, the study demonstrated that these signals improve categorization performance, training speed, and noise robustness of classifier networks. This work, highlighting the role of self-generated movements in perceptual stability, was published in Plos Comp. Biology. Finally, Dr. Bolanos's Ph.D. research on texture processing in the mouse visual cortex has been accepted for publication pending minor revisions in Nature Communications.
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| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
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