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

  • Tsukasa TsuyukiEmail author
  • Yuki Yamamoto
  • Tadashi Yamazaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

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.

Keywords

Computer simulation Spatial model Graphics processing units Conjugate gradient method 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tsukasa Tsuyuki
    • 1
    Email author
  • Yuki Yamamoto
    • 2
  • Tadashi Yamazaki
    • 1
    • 3
  1. 1.Graduate School of Informatics and EngineeringThe University of Electro-CommunicationsTokyoJapan
  2. 2.Faculty of MedicineTokyo Medical and Dental UniversityTokyoJapan
  3. 3.RIKEN Brain Science InstituteNeuroinformatics Japan CenterSaitamaJapan

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