Training Delays in Spiking Neural Networks

  • Laura StateEmail author
  • Pau Vilimelis Aceituno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11727)


Spiking Neural Networks (SNNs) are a promising computational paradigm, both to understand biological information processing and for low-power, embedded chips. Although SNNs are known to encode information in the precise timing of spikes, conventional artificial learning algorithms do not take this into account directly. In this work, we implement the spike timing by training the synaptic delays in a single layer SNN. We use two different approaches: a classical gradient descent and a direct algebraic method that is based on a complex-valued encoding of the spikes. Both algorithms are equally able to correctly solve simple detection tasks. Our work provides new optimization methods for the data analysis of highly time-dependent data and training methods for neuromorphic chips.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of TübingenTübingenGermany
  2. 2.Max Planck Institute for Mathematics in the SciencesLeipzigGermany

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