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Experience-dependent change of visual feature representation in mouse visual cortex

Research Project

Project/Area Number 17K13276
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Brain biometrics
Research InstitutionThe University of Tokyo

Principal Investigator

Yoshida Takashi  東京大学, 大学院医学系研究科(医学部), 助教 (30723259)

Research Collaborator OHKI Kenichi  
UKITA Jumpei  
HAYASHI Ayako  
Project Period (FY) 2017-04-01 – 2019-03-31
Project Status Completed (Fiscal Year 2018)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Keywordsデコーディング / 大脳皮質 / 視覚野 / マウス / 2光子イメージング / 画像再構成 / 集団活動 / ポピュレーションコーディング / 大脳皮質視覚野 / 画像復元 / 学習 / イメージング
Outline of Final Research Achievements

Main purpose of this study is to understand what types of information is represented by sparsely active cortical neurons and whether information is reliably represented despite variability of neuronal responses. We have revealed that a complex pattern of a single natural image is mainly represented by a small number of strongly active neurons and that image representation is reliable regardless of variability of neuronal responses, which is achieved by some neurons that show similar representation patterns but are active on different trials. These results suggest an optimal decoding strategy in which downstream areas can reliably extract image information by collecting responses of strongly active neurons. We have also examined how image representation is modified by learning.

Academic Significance and Societal Importance of the Research Achievements

大脳皮質での情報表現の特徴の一つとして、少数の細胞により情報が表現されるスパースコーディングという考えが提唱されている。理論的な観点からスパースコーディングの利点が提唱されているが、実際の脳内の神経活動はばらつきが大きく、少数の細胞にどのような情報がどの程度安定して表現されているかを実験的に調べた研究はほとんどなかった。本研究の意義としては、生体脳において自然画像のパターンが少数の神経細胞に表現されていること、また、神経活動は比較的不安定であるにもかかわらず、情報が安定して表現されていることを明らかにした点にある。この成果は新規の画像処理や情報処理技術の開発への応用の可能性が期待される。

Report

(3 results)
  • 2018 Annual Research Report   Final Research Report ( PDF )
  • 2017 Research-status Report
  • Research Products

    (3 results)

All 2019 2018

All Journal Article (3 results) (of which Peer Reviewed: 2 results,  Open Access: 3 results)

  • [Journal Article] Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network2019

    • Author(s)
      Ukita Jumpei、Yoshida Takashi、Ohki Kenichi
    • Journal Title

      Scientific Reports

      Volume: 9 Issue: 1

    • DOI

      10.1038/s41598-019-40535-4

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Cell Type Specific Representation of Vibro-tactile Stimuli in the Mouse Primary Somatosensory Cortex2018

    • Author(s)
      Hayashi Ayako、Yoshida Takashi、Ohki Kenichi
    • Journal Title

      Frontiers in Neural Circuits

      Volume: 12

    • DOI

      10.3389/fncir.2018.00109

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Robust representation of natural images by sparse and variable population of active neurons in visual cortex2018

    • Author(s)
      Yoshida Takashi、Ohki Kenichi
    • Journal Title

      BioRxiv

      Volume: NA

    • DOI

      10.1101/300863

    • Related Report
      2018 Annual Research Report
    • Open Access

URL: 

Published: 2017-04-28   Modified: 2020-03-30  

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