Journal of Computational Neuroscience

, Volume 32, Issue 2, pp 309–326 | Cite as

Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks

  • Stephan Henker
  • Johannes PartzschEmail author
  • René Schüffny


With the various simulators for spiking neural networks developed in recent years, a variety of numerical solution methods for the underlying differential equations are available. In this article, we introduce an approach to systematically assess the accuracy of these methods. In contrast to previous investigations, our approach focuses on a completely deterministic comparison and uses an analytically solved model as a reference. This enables the identification of typical sources of numerical inaccuracies in state-of-the-art simulation methods. In particular, with our approach we can separate the error of the numerical integration from the timing error of spike detection and propagation, the latter being prominent in simulations with fixed timestep. To verify the correctness of the testing procedure, we relate the numerical deviations to theoretical predictions for the employed numerical methods. Finally, we give an example of the influence of simulation artefacts on network behaviour and spike-timing-dependent plasticity (STDP), underlining the importance of spike-time accuracy for the simulation of STDP.


Neural network simulation Numerical accuracy Integrate-and-fire STDP 



The research leading to these results has received funding from the European Union 7th Framework Programme (FP7/2007-2013) under grant agreement no. 269921 (BrainScaleS).

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Stephan Henker
    • 1
  • Johannes Partzsch
    • 1
    Email author
  • René Schüffny
    • 1
  1. 1.Endowed Chair for Parallel VLSI Systems and Neural Circuits Institute of Circuits and SystemsUniversity of Technology DresdenDresdenGermany

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