| Project/Area Number |
18K06328
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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 44050:Animal physiological chemistry, physiology and behavioral biology-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Hiroi Makoto 東京大学, 定量生命科学研究所, 助教 (80597831)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | associative memory / olfaction / Drosophila / 連想記憶 / リカレント回路 / ショウジョウバエ / キノコ体 / 嗅覚 / 連合学習 / 光遺伝学 / 記憶 / 記憶学習 |
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| Outline of Final Research Achievements |
To tune the intensity and temporal control of the conditioned stimuli, we have set up a system of associative learning that artificially activates a group of nerves expressing arbitrary olfactory receptors by means of optogenetics. It is now easier to analyze recurrent circuits by activating dopaminergic nerves. This year, we tested several conditions of the timing of each stimulus in addition to the experimental time, light intensity, and odor concentration for the device of behavioral experiments for photogenetics, and explored the conditions under which the learning scores obtained are stable. By artificially changing olfactory neurons to be activated, a variety of learning performances from low to high scores were confirmed. By changing the dopaminergic neurons activated during conditioning, we were able to achieve stable associative learning in both avoidance and reward learning.
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| Academic Significance and Societal Importance of the Research Achievements |
数百におよぶ脳神経の生理活性を同時記録し自発的な活性から学習依存的に変化する神経応答を解析するためには、厳密な実験条件の検討とデータ取得が必須である。本研究は、光遺伝学的手法による特定神経の活性化を用いることで、嗅覚神経入力を高い精度で制御することができた。また、キノコ体神経のカルシウム応答を学習前後にわたって安定して記録できる系を組み合わせた。このスキームは嗅覚関連学習だけでなく幅広い分野で活用できる。
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