Advertisement

GPU-Accelerated Simulations of an Electric Stimulus and Neural Activities in Electrolocation

  • Kazuhisa Fujita
  • Yoshiki Kashimori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

To understand mechanism of information processing by a neural network, it is important to well know a sensory stimulus. However, it is hard to examine details of a real stimulus received by an animal. Furthermore, it is too hard to simultaneously measure a received stimulus and neural activities of a neural system. We have studied the electrosensory system of an electric fish in electrolocation. It is also difficult to measure the electric stimulus received by an electric fish in the real environment and neural activities evoked by the electric stimulus. To address this issue, we have applied computational simulation. We developed the simulation software accelerated by a GPU to calculate various electric stimuli and neural activities of the electrosensory system using a GPU. This paper describes comparison of computation time between CPUs and a GPU in calculation of the electric field and the neural activities.

Keywords

GPGPU CUDA Acceleration Electrolocation 

Notes

Acknowledgment

This work was supported by JSPS KAKENHI Grant Number 15K07146.

References

  1. 1.
    Beyeler, M., Oros, N., Dutt, N., Krichmar, J.L.: A GPU-accelerated cortical neural network model for visually guided robot navigation. Neural Netw. Official J. Int. Neural Netw. Soc. 72, 75–87 (2015)CrossRefGoogle Scholar
  2. 2.
    Brandman, R., Nelson, M.E.: A simple model of long-term spike train regularization. Neural Comput. 14(7), 1575–1597 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Doiron, B., Laing, C., Longtin, A., Maler, L.: Ghostbursting: a novel neuronal burst mechanism. J. Comput. Neurosci. 12(1), 5–25 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Fujita, K., Kashimori, Y.: Modeling the electric image produced by objects with complex impedance in weakly electric fish. Biol. Cybern. 103(2), 105–118 (2010)CrossRefGoogle Scholar
  5. 5.
    Heiligenberg, W.: Theoretical and experimental approaches to spatial aspects of electrolocation. J. Comp. Physiol. 103(3), 247–272 (1975)CrossRefGoogle Scholar
  6. 6.
    Hoshimiya, N., Shogen, K., Matsuo, T., Chichibu, S.: The apteronotus EOD field: waveform and EOD field simulation. J. Comp. Physiol. 135, 283–290 (1980)CrossRefGoogle Scholar
  7. 7.
    Yamazaki, T., Igarashi, J.: Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit. Neural Netw. 47, 103–111 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.National Institute of TechnologyTsuyama CollageTsuyamaJapan
  2. 2.University of Electro-CommunicationsChofuJapan

Personalised recommendations