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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 244–251Cite as

Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9950)

Abstract

In temporal information processing, both the cerebellum and the basal ganglia play essential roles. In particular, for interval timing learning, the cerebellum exhibits temporally localized activity around the onset of the unconditioned stimulus, whereas the basal ganglia represents the passage of time by their ramping-up activity from the onset of the conditioned stimulus to that of the unconditioned stimulus. We present a unified computational model of the cerebellum and the basal ganglia for the interval timing learning task. We report that our model reproduces the localized activity in the cerebellum and the gradual increase of the activity in the basal ganglia. These results suggest that the cerebellum and the basal ganglia play different roles in temporal information processing.

Keywords

  • Computational model
  • Cerebellum
  • Basal ganglia
  • Interval timing
  • Relay hypothesis

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Acknowledgment

We would like to thank Professor Masaki Tanaka at Hokkaido University for fruitful discussions on his relay hypothesis. Part of this work was supported by JSPS KAKENHI Grant Number 26119511. This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

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Authors and Affiliations

  1. Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

    Ohki Katakura & Tadashi Yamazaki

  2. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Tadashi Yamazaki

Authors
  1. Ohki Katakura
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  2. Tadashi Yamazaki
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Corresponding author

Correspondence to Ohki Katakura .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Katakura, O., Yamazaki, T. (2016). Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

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