Skip to main content
SpringerLink
Log in

Comparison of learning methods for spiking neural networks

  • Published:
Optical Memory and Neural Networks Aims and scope Submit manuscript

Abstract

Investigation of different factor influence on the spike-timing-dependent plasticity learning process was performed. The next factors were analyzed: choice of spike pairing scheme, shapes of postsynaptic currents and the choice of input type signal for learning. Best factors for learning performance were extracted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Maass, W., Networks of spiking neurons: the third generation of neural network models, Neural Networks, 1997, vol. 10, no. 9, pp. 1659–1671.

    Article  Google Scholar 

  2. Bichler, O., Querlioz, D., Thorpe, S.J., Bourgoin, J.-P., and Gamrat, C., Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity, Neural Networks, 2012, no. 32, pp. 339–348.

    Google Scholar 

  3. Pichevar, R. and Rouat, J., Monophonic sound source separation with an unsupervised network of spiking neurons, Neurocomputing, 2007, no. 71, pp.109–120.

    Google Scholar 

  4. Chandhok, C. and Chaturvedi, S., Adaptation of spiking neural networks for image clustering, Int. J. Video Image Process. Network Security IJVIPNS-IJENS, 2003, no. 12.

    Google Scholar 

  5. Maass, W. and Markram, H., Synapses as dynamic memory buffers, Neural Networks, 2002, no. 15, pp. 155–161.

    Google Scholar 

  6. Gewaltig, M.-O. and Diesmann, M., NEST (Neural Simulation Tool), Scholarpedia, 2007, vol. 2, no. 4, p. 1430.

    Article  Google Scholar 

  7. Morrison, A., Diesmann, M., and Gerstner, W., Phenomenological models of synaptic plasticity based on spike timing, Biol. Cybernetics, 2008, vol. 98, no. 6, pp. 459–78.

    Article  MathSciNet  MATH  Google Scholar 

  8. CSIM: A neural circuit SIMulator, The IGI LSM Group, Technical University, Graz 2002; URL: http://www.lsm.tugraz.at.

  9. Legenstein, R., Naeger, C., and Maass, W., What can a neuron learn with spike-timing-dependent plasticity?, Neural Computation, 2005, vol. 17, pp. 2337–2382.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Research Centre “Kurchatov institute”, Moscow, 123182, Russia

    K. Kukin & A. Sboev

Authors
  1. K. Kukin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Sboev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Kukin.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kukin, K., Sboev, A. Comparison of learning methods for spiking neural networks. Opt. Mem. Neural Networks 24, 123–129 (2015). https://doi.org/10.3103/S1060992X15020095

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1060992X15020095

Keywords

Search

Navigation