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Bihemispheric Cerebellar Spiking Network Model to Simulate Acute VOR Motor Learning

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Neural Information Processing (ICONIP 2016)

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

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Abstract

The vestibuloocular reflex (VOR) is an adaptive control system. The cerebellar flocculus is intimately involved in the VOR adaptive motor control. The cerebellar flocculus has bihemispheric architecture and the several lines of unilateral lesion study indicated that each cerebellar hemisphere plays different roles in the leftward and rightward eye movement control and learning. However, roles of bihemispheric cerebellar architecture underlying the VOR motor learning have not been fully understood. Here we configure an anatomically/physiologically plausible bihemispheric cerebellar neuronal network model composed of spiking neurons as a platform to unveil roles and capacities of bihemispheric cerebellar architecture in the VOR motor learning.

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Acknowledgements

This work was supported in part by JSPS KAKENHI Grant-In-Aid for Scientific Research (B) (24300115 and 16H02901, YH) and Grant-in-Aid for Young Scientists (B) (15K16086, KI).

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Authors and Affiliations

  1. Department of Robotic Science and Technology, College of Engineering, Chubu University, Kasugai, Japan

    Keiichiro Inagaki & Yutaka Hirata

Authors
  1. Keiichiro Inagaki
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  2. Yutaka Hirata
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Corresponding author

Correspondence to Keiichiro Inagaki .

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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Inagaki, K., Hirata, Y. (2016). Bihemispheric Cerebellar Spiking Network Model to Simulate Acute VOR Motor 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_31

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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