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
    • Ecole Normale Supérieure
  • Michelle Rudolph
    • CNRS
  • Ted Carnevale
    • Yale University
  • Michael Hines
    • Yale University
  • David Beeman
    • University of Colorado
  • James M. Bower
    • University of Texas
  • Markus Diesmann
    • University of Freiburg
    • RIKEN Brain Science Institute
  • Abigail Morrison
    • RIKEN Brain Science Institute
  • Philip H. Goodman
    • University of Nevada
  • Frederick C. HarrisJr.
    • University of Nevada
  • Milind Zirpe
    • University of Nevada
  • Thomas Natschläger
    • Software Competence Center Hagenberg
  • Dejan Pecevski
    • Technical University of Graz
  • Bard Ermentrout
    • University of Pittsburgh
  • Mikael Djurfeldt
    • KTH
  • Anders Lansner
    • KTH
  • Olivier Rochel
    • University of Leeds
  • Thierry Vieville
    • INRIA
  • Eilif Muller
    • Kirchhoff Institute for Physics
  • Andrew P. Davison
    • CNRS
  • Sami El Boustani
    • CNRS
    • CNRS
    • Unité de Neurosciences Intégratives, et Computationnelles (UNIC)CNRS (Bat 33)
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


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.


Spiking neural networksSimulation toolsIntegration strategiesClock-drivenEvent-driven
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© Springer Science+Business Media, LLC 2007