Optical Memory and Neural Networks

, Volume 24, Issue 2, pp 123–129 | Cite as

Comparison of learning methods for spiking neural networks

Article

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.

Keywords

spiking neural network learning methods neurosimulators STDP spike-pairing scheme 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Maass, W., Networks of spiking neurons: the third generation of neural network models, Neural Networks, 1997, vol. 10, no. 9, pp. 1659–1671.CrossRefGoogle Scholar
  2. 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. 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. 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. 5.
    Maass, W. and Markram, H., Synapses as dynamic memory buffers, Neural Networks, 2002, no. 15, pp. 155–161.Google Scholar
  6. 6.
    Gewaltig, M.-O. and Diesmann, M., NEST (Neural Simulation Tool), Scholarpedia, 2007, vol. 2, no. 4, p. 1430.CrossRefGoogle Scholar
  7. 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.MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    CSIM: A neural circuit SIMulator, The IGI LSM Group, Technical University, Graz 2002; URL: http://www.lsm.tugraz.at.
  9. 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.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Allerton Press, Inc. 2015

Authors and Affiliations

  1. 1.National Research Centre “Kurchatov institute”MoscowRussia

Personalised recommendations