Journal of Computational Neuroscience

, Volume 23, Issue 3, pp 349–398

Simulation of networks of spiking neurons: A review of tools and strategies

  • Romain Brette
  • Michelle Rudolph
  • Ted Carnevale
  • Michael Hines
  • David Beeman
  • James M. Bower
  • Markus Diesmann
  • Abigail Morrison
  • Philip H. Goodman
  • Frederick C. HarrisJr.
  • Milind Zirpe
  • Thomas Natschläger
  • Dejan Pecevski
  • Bard Ermentrout
  • Mikael Djurfeldt
  • Anders Lansner
  • Olivier Rochel
  • Thierry Vieville
  • Eilif Muller
  • Andrew P. Davison
  • Sami El Boustani
  • Alain Destexhe
Topical Review on Techniques

DOI: 10.1007/s10827-007-0038-6

Cite this article as:
Brette, R., Rudolph, M., Carnevale, T. et al. J Comput Neurosci (2007) 23: 349. doi:10.1007/s10827-007-0038-6

Abstract

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

Keywords

Spiking neural networksSimulation toolsIntegration strategiesClock-drivenEvent-driven

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Romain Brette
    • 1
  • Michelle Rudolph
    • 2
  • Ted Carnevale
    • 3
  • Michael Hines
    • 3
  • David Beeman
    • 4
  • James M. Bower
    • 5
  • Markus Diesmann
    • 6
    • 7
  • Abigail Morrison
    • 7
  • Philip H. Goodman
    • 8
  • Frederick C. HarrisJr.
    • 8
  • Milind Zirpe
    • 8
  • Thomas Natschläger
    • 9
  • Dejan Pecevski
    • 10
  • Bard Ermentrout
    • 11
  • Mikael Djurfeldt
    • 12
  • Anders Lansner
    • 12
  • Olivier Rochel
    • 13
  • Thierry Vieville
    • 14
  • Eilif Muller
    • 15
  • Andrew P. Davison
    • 2
  • Sami El Boustani
    • 2
  • Alain Destexhe
    • 2
    • 16
  1. 1.Ecole Normale SupérieureParisFrance
  2. 2.CNRSGif-sur-YvetteFrance
  3. 3.Yale UniversityNew HavenUSA
  4. 4.University of ColoradoBoulderUSA
  5. 5.University of TexasSan AntonioUSA
  6. 6.University of FreiburgFreiburgGermany
  7. 7.RIKEN Brain Science InstituteWako CityJapan
  8. 8.University of NevadaRenoUSA
  9. 9.Software Competence Center HagenbergHagenbergAustria
  10. 10.Technical University of GrazGrazAustria
  11. 11.University of PittsburghPittsburghUSA
  12. 12.KTHStockholmSweden
  13. 13.University of LeedsLeedsUK
  14. 14.INRIANiceFrance
  15. 15.Kirchhoff Institute for PhysicsHeidelbergGermany
  16. 16.Unité de Neurosciences Intégratives, et Computationnelles (UNIC)CNRS (Bat 33)Gif-sur-YvetteFrance