| Project/Area Number |
17K14933
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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 |
Neurophysiology / General neuroscience
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Kambara Hiroyuki 東京工業大学, 科学技術創成研究院, 助教 (50451993)
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | 運動学習 / 筋シナジー / 計算論的神経科学 |
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| Outline of Final Research Achievements |
Muscle synergy is one of the neural mechanism that makes complex movement easier for brain to control. Although recent studies showed that activity pattern of multiple muscles can be reconstructed by linear sum of fewer number of synergy components, it is still unknown how our brain modulate muscle synergies to control our body efficiently in various situations. In this study, we made a hypothesis that muscle synergies are modulated by reinforcement algorithm, and propose a motor control model which can learn muscle synergies, their activation level, and internal forward model of musculoskeletal system. By applying our model to motor control task so called virtual surgery task, we verified that our can reproduce qualitative results of the past virtual surgery task experiments. It is suggested that the reason that modulation of muscle synergies takes more time than that of synergy activation level may originated from the difference of learning algorithm adopted by our brain.
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| Academic Significance and Societal Importance of the Research Achievements |
筋シナジーの学習は、その活動レベルの学習に比べて時間がかかることが過去の研究によって観測されてきた。この運動学習に関する特徴を説明する仮説として、両者の学習率の違いが提案されてきた。一方、本研究により、両者の学習速度の違いは学習率の違いによってではなく、学習アルゴリズムそのものが異なる可能性が示唆され、適切な運動を生成するための運動学習に関する脳内情報 処理のメカニズムの解明に役立つと考えられる。また、筋シナジーは運動を作り出す筋活動の基底をなす要素であり、本研究で提案した運動学習モデルに基づいた効果的な運動トレーニング方法を開発につながることが期待できる。
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