Spiking Neurons on GPUs

  • Fabrice Bernhard
  • Renaud Keriven
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


Simulating large networks of spiking neurons is a very common task in the areas of Neuroinformatics and Computational Neurosciences. These simulations are time-consuming but also often intrinsically parallel. The recent advent of powerful and programmable graphic cards seems to be a pertinent solution to the problem: they offer a cheap but efficient possibility to serve as very fast co-processors for the parallel computing that spiking neural networks need. We describe our implementation of three different problems on such a card: two image-segmentation algorithms using spiking neural networks and one multi-purpose spiking neural-network simulator. Using these examples we show the benefits, the challenges and the limits of such an implementation.


Neural Network Graphic Processing Unit Graphic Card Spike Neuron Conditional Branch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fabrice Bernhard
    • 1
  • Renaud Keriven
    • 1
  1. 1.Projet Odyssée – INRIA/ENS/ENPCParisFrance

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