| Project/Area Number |
18KK0286
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| Research Category |
Fund for the Promotion of Joint International Research (Fostering Joint International Research (B))
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 62:Applied informatics and related fields
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| Research Institution | Chubu University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
加藤 明 東海大学, 医学部, 准教授 (70546746)
LEE Jaeryoung 中部大学, 工学部, 講師 (70736363)
小野 誠司 筑波大学, 体育系, 教授 (70754753)
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| Project Period (FY) |
2018-10-09 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2020: ¥7,280,000 (Direct Cost: ¥5,600,000、Indirect Cost: ¥1,680,000)
Fiscal Year 2019: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
Fiscal Year 2018: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
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| Keywords | 予測性制御 / 眼球運動 / ロボット / 人工小脳 |
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| Outline of Final Research Achievements |
In this study, we advanced our research across four levels. ①Behavioral Level: We confirmed various characteristics of predictive OKR (Optokinetic Response) previously demonstrated in goldfish in mice, monkeys, and humans. ②Neural Activity Level: In goldfish, we showed that some Purkinje cells in the cerebellum exhibit changes in firing frequency synchronized with eye velocity oscillations in the dark after acquiring predictive OKR. ③Neural Network Level: Based on the neural pathways involved in OKR, we constructed an anatomically realistic neural circuit model. ④Engineering Application Level: We implemented the model constructed in ③ as an artificial cerebellum on hardware (FPGA) and applied it to real-time adaptive robot control, demonstrating its capabilities and effectiveness.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は２つの学術的意義を有する．第一に，一研究室では実現困難な複数の動物種における同一の行動（OKR）を対象とし，未だ神経科学的に理解の進んでいなかった予測性適応運動制御の獲得メカニズムを明らかにした点にある．第二に，実験と計算論を融合したアプローチをとり, さらに, 得られた知見を統合して実機制御に応用することにより，その工学的有効性を検証した点が挙げられる．これらの成果は，今後のロボット共存社会における環境適応型ロボットの制御技術として応用が期待でき，社会的にも大きな意義を持つものと考えられる．
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