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Efficient Numerical Simulation of Neuron Models with Spatial Structure on Graphics Processing Units

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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

Computer simulation of multi-compartment neuron models is difficult, because writing the computer program is tedious but complicated, and it requires sophisticated numerical methods to solve partial differential equations (PDEs) that describe the current flow in a neuron robustly. For this reason, dedicated simulation software such as NEURON and GENESIS have been used widely. However, these simulators do not support hardware acceleration using graphics processing units (GPUs). In this study, we implemented a conjugate gradient (CG) method to solve linear equations efficiently on a GPU in our own software. CG methods are known much faster and more efficient than the Gaussian elimination, when the matrix is huge and sparse. As a result, our software succeeded to carry out a simulation of Purkinje cells developed by De Schutter and Bower (1994) on a GPU. The GPU (Tesla K40c) version realized 3 times faster computation than that a single-threaded CPU version for 15 Purkinje cells.

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Acknowledgments

Part of this study was supported by JSPS KAKENHI Grant Number 26430009.

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Authors and Affiliations

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

    Tsukasa Tsuyuki & Tadashi Yamazaki

  2. Faculty of Medicine, Tokyo Medical and Dental University, Tokyo, Japan

    Yuki Yamamoto

  3. RIKEN Brain Science Institute, Neuroinformatics Japan Center, Saitama, Japan

    Tadashi Yamazaki

Authors
  1. Tsukasa Tsuyuki
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  2. Yuki Yamamoto
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  3. Tadashi Yamazaki
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Corresponding author

Correspondence to Tsukasa Tsuyuki .

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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Tsuyuki, T., Yamamoto, Y., Yamazaki, T. (2016). Efficient Numerical Simulation of Neuron Models with Spatial Structure on Graphics Processing Units. 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_34

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_34

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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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