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2020 年度 実施状況報告書

Experimental and theoretical investigation of Bayesian inference across multiple layers in somatosensory cortex

研究課題

研究課題/領域番号 19K20348
研究機関沖縄科学技術大学院大学

研究代表者

LI Yuzhe  沖縄科学技術大学院大学, 神経計算ユニット, 研究員 (30815436)

研究期間 (年度) 2019-04-01 – 2022-03-31
キーワードcalcium imaging / prism lens / functional connectivity / transfer entropy / multiple cortical layers / clustering
研究実績の概要

1. The new novelly designed setup has been finalized. The new setup, which includes the 3D printed body parts, the motor and sensor circuits, and the control and recording programs, is designed and achieved by myself. The control program and recording program are developed based on python, which is able to achieve fixed sampling-rate recording and task control parallelly. The new control paradigm includes a fluctuation to the motor, which will introduce uncertainty to mice’s sensory input
2. Achieved clear multiple-layer calcium imaging, after failures on more than 80 mice.
3. Achieved analysis on multiple layer recordings of resting states, which includes transfer entropy, HMM analysis and probabilistic PCA for neuron clustering.

現在までの達成度 (区分)
現在までの達成度 (区分)

2: おおむね順調に進展している

理由

1. It takes a bit more time in developing the new setups. The details of the models for 3D printing were modified many times according to testing with animals.
2. After a lot of failures, the multiple layer imaging is finally well taken.
3. Analysis on a dataset that contains sequential multiple layer scanning by 2-photon calcium imaging was done, which is a bit different from the dataset from this experiment, but the methods I developed for the analysis can be shared with this project.

今後の研究の推進方策

1. Animal training with tasks need to be done.
2. The Bayesian model and deep learning model analogue are worth to try on the neural recordings taken during the tasks.

次年度使用額が生じた理由

1. The new animal experiments and surgeries need to purchase more virus and lens.
2. An extra computer needs to be purchased for separating the task control and data recording to avoid frame dropping problem.
3. To accelerate training, a second setup needs to be made, which needs to purchase more printing materials, and electronic elements.

  • 研究成果

    (4件)

すべて 2020

すべて 学会発表 (4件) (うち国際学会 2件、 招待講演 1件)

  • [学会発表] Investigation of temporal and spatial origination of neural network in sensory cortex2020

    • 著者名/発表者名
      Yuzhe Li
    • 学会等名
      The 30th Annual Conference of Japanese Neural Network Society
    • 国際学会
  • [学会発表] Neuron hubs distributed differently in deep layers and superficial layers in different brain states. The 1st Asia-Pacific Computational and Cognitive Neuroscience Conference2020

    • 著者名/発表者名
      Yuzhe Li
    • 学会等名
      The 1st Asia-Pacific Computational and Cognitive Neuroscience Conference
    • 国際学会
  • [学会発表] Extracting information flow across cortical layers from multi-depth two-photon imaging data2020

    • 著者名/発表者名
      Yuzhe Li
    • 学会等名
      第63回自動制御連合講演会-講演論文集
  • [学会発表] Investigation of information flow and temporal-spatial organization of neurons across cortical layers from multi-depth two-photon imaging data2020

    • 著者名/発表者名
      Yuzhe Li
    • 学会等名
      データ駆動生物学ワークショップ
    • 招待講演

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公開日: 2021-12-27  

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