Advertisement

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)

Abstract

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.

References

  1. 1.
    Bishop, C.M.: Pattern Recognition and Machine Learing. Springer, New York (2006)Google Scholar
  2. 2.
    Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vis. 113(1), 54–66 (2014).  https://doi.org/10.1007/s11263-014-0788-3MathSciNetCrossRefGoogle Scholar
  3. 3.
    Davies, M., et al.: Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82–99 (2018).  https://doi.org/10.1109/mm.2018.112130359CrossRefGoogle Scholar
  4. 4.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press (2001)Google Scholar
  5. 5.
    Diehl, P.U., Zarrella, G., Cassidy, A., Pedroni, B.U., Neftci, E.: Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In: 2016 IEEE International Conference on Rebooting Computing (ICRC), IEEE, October 2016.  https://doi.org/10.1109/icrc.2016.7738691
  6. 6.
    Guyonneau, R., VanRullen, R., Thorpe, S.J.: Neurons tune to the earliest spikes through STDP. Neural Comput. 17(4), 859–879 (2005).  https://doi.org/10.1162/0899766053429390CrossRefzbMATHGoogle Scholar
  7. 7.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998).  https://doi.org/10.1109/5.726791CrossRefGoogle Scholar
  8. 8.
    O’Connor, P., Welling, M.: Deep spiking networks. arxiv (2016)Google Scholar

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

Personalised recommendations