Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional components reduces dimensions for reinforcement learning

  1. Huu Hoang
  2. Shinichiro Tsutsumi
  3. Masanori Matsuzaki
  4. Masanobu Kano
  5. Mitsuo Kawato
  6. Kazuo Kitamura  Is a corresponding author
  7. Keisuke Toyama  Is a corresponding author
  1. Advanced Telecommunications Research Institute International, Japan
  2. RIKEN Center for Brain Science, Japan
  3. The University of Tokyo, Japan
  4. University of Yamanashi, Japan
  5. ATR Neural Information Analysis Laboratories, Japan

Abstract

Cerebellar climbing fibers convey diverse signals, but how they are organized in the compartmental structure of the cerebellar cortex during learning remains largely unclear. We analyzed a large amount of coordinate-localized two-photon imaging data from cerebellar Crus II in mice undergoing 'Go/No-go' reinforcement learning. Tensor component analysis revealed that a majority of climbing fiber inputs to Purkinje cells were reduced to only four functional components, corresponding to accurate timing control of motor initiation related to a Go cue, cognitive error-based learning, reward processing, and inhibition of erroneous behaviors after a No-go cue. Changes in neural activities during learning of the first two components were correlated with corresponding changes in timing control and error learning across animals, indirectly suggesting causal relationships. Spatial distribution of these components coincided well with boundaries of Aldolase-C/zebrin II expression in Purkinje cells, whereas several components are mixed in single neurons. Synchronization within individual components was bidirectionally regulated according to specific task contexts and learning stages. These findings suggest that, in close collaborations with other brain regions including the inferior olive nucleus, the cerebellum, based on anatomical compartments, reduces dimensions of the learning space by dynamically organizing multiple functional components, a feature that may inspire new-generation AI designs.

Data availability

Data analysed for all the figures are included in the manuscript and source data files. The Aldoc-tdTomato mouse line is available at RIKEN Bio Resource Center (RBRC10927).

The following previously published data sets were used

Article and author information

Author details

  1. Huu Hoang

    Computational Brain Imaging, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1463-8323
  2. Shinichiro Tsutsumi

    RIKEN Center for Brain Science, Saitama, Japan
    Competing interests
    The authors declare that no competing interests exist.
  3. Masanori Matsuzaki

    Department of Physiology, The University of Tokyo, Tokyo, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3872-4322
  4. Masanobu Kano

    Department of Neurophysiology, The University of Tokyo, Tokyo, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0725-3292
  5. Mitsuo Kawato

    Computational Brain Imaging, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  6. Kazuo Kitamura

    Department of Neurophysiology, University of Yamanashi, Yamanashi, Japan
    For correspondence
    kitamurak@yamanashi.ac.jp
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8956-4122
  7. Keisuke Toyama

    Neural Information Analysis Laboratories, ATR Neural Information Analysis Laboratories, Kyoto, Japan
    For correspondence
    toyama@atr.jp
    Competing interests
    The authors declare that no competing interests exist.

Funding

Japan Society for the Promotion of Science

  • Huu Hoang
  • Masanori Matsuzaki
  • Masanobu Kano
  • Mitsuo Kawato
  • Kazuo Kitamura
  • Keisuke Toyama

Japan Science and Technology Agency

  • Huu Hoang
  • Keisuke Toyama

Japan Agency for Medical Research and Development

  • Huu Hoang
  • Mitsuo Kawato
  • Kazuo Kitamura

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All experiments were approved by the Animal Experiment Committees of the University of Tokyo (#P08-015) and the University of Yamanashi (#A27-1), and carried out in accordance with national regulations and institutional guidelines.

Copyright

© 2023, Hoang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Huu Hoang
  2. Shinichiro Tsutsumi
  3. Masanori Matsuzaki
  4. Masanobu Kano
  5. Mitsuo Kawato
  6. Kazuo Kitamura
  7. Keisuke Toyama
(2023)
Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional components reduces dimensions for reinforcement learning
eLife 12:e86340.
https://doi.org/10.7554/eLife.86340

Share this article

https://doi.org/10.7554/eLife.86340

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