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Implementing Fuzzy Reasoning on a Spiking Neural Network

  • Cornelius Glackin
  • Liam McDaid
  • Liam Maguire
  • Heather Sayers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)

Abstract

This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies. The receptive fields behave in a similar manner as fuzzy membership functions. The network is supervised but learning only occurs locally as in the biological case. The connectivity of the hidden and output layers is representative of a fuzzy rule base. The advantages and disadvantages of the network topology for the IRIS classification task are demonstrated and directions of current and future work are discussed.

Keywords

Spiking Neuron Model Dynamic Synapse Supervised Learning Receptive Field Fuzzy Reasoning 

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References

  1. 1.
    Maass, W.: Networks of Spiking Neurons: The Third Generation of Neural Network Models. Electronic Colloquium on Computational Complexity (ECCC) 3(31) (1996)Google Scholar
  2. 2.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nature Neuroscience 2, 1178–1183 (2000)CrossRefGoogle Scholar
  4. 4.
    Bohte, S.M., Kok, J.N., La Poutre, H.: Error-backpropagation in Temporally Encoded Networks of Spiking Neurons. Neurocomputing 48, 17–37 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Belatreche, A., Maguire, L.P., McGinnity, T.M., Wu, Q.X.: A Method for Supervised Training of Spiking Neural Networks. In: IEEE Cybernetics Intelligence. Challenges and Advances (CICA), pp. 39–44 (2003)Google Scholar
  6. 6.
    Sougne, J.P.: A learning algorithm for synfire chains. Connectionist Models of Learning, Development and Evolution, pp. 23–32 (2001)Google Scholar
  7. 7.
    Ruf, B., Schmitt, M.: Learning temporally encoded patterns in networks of spiking neurons. Neural Processing Letters 5(1), 9–18 (1997)CrossRefGoogle Scholar
  8. 8.
    Carnell, A., Richardson, D.: Linear algebra for time series of spikes. In: 13th European Symposium on Artificial Neural Networks (ESANN) (2005)Google Scholar
  9. 9.
    Pfister, J.P., Barber, D., Gerstner, W.: Optimal Hebbian Learning: A Probabilistic Point of View. In: ICANN/ICONIP Lecture Notes in Computer Science, vol. 2714, pp. 92–98 (2003)Google Scholar
  10. 10.
    Kasinski, A., Ponulak, F.: Comparison of Supervised Learning Methods for Spike Time Coding in Spiking Neural Networks (2005), http://matwbn.icm.edu.pl/ksiazki/amc/amc16/amc1617.pdf
  11. 11.
    Tsodyks, M., Pawelzik, K., Markram, H.: Neural Networks with Dynamic Synapses. Neural Computation 10(4), 821–835 (1998)CrossRefGoogle Scholar
  12. 12.
    Natschlager, T., Maass, W., Zador, A.: Efficient temporal processing with biologically realistic dynamic synapses. Network: Computation in Neural Systems 12, 75–87 (2001)CrossRefGoogle Scholar
  13. 13.
    Barlow, H.B.: Summation and inhibition in the frogfs retina. J. Physiol. 119, 69–88 (1953)Google Scholar
  14. 14.
    Fischer, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)Google Scholar
  15. 15.
    Abdelbar, A.M., Hassan, D.O., Tagliarini, G.A., Narayan, S.: Receptive Field Optimisation for Ensemble Encoding. Neural. Comput. & Applic. 15(1), 1–8 (2006)CrossRefGoogle Scholar
  16. 16.
    Dunn, J.: A fuzzy relative of the ISODATA process and its use in detecting compact wellseparated clusters. Journal of Cybernetics 3, 32–57 (1973)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cornelius Glackin
    • 1
  • Liam McDaid
    • 1
  • Liam Maguire
    • 1
  • Heather Sayers
    • 1
  1. 1.Faculty of Engineering, School of Computing & Intelligent SystemsUniversity of UlsterLondonderryNorthern Ireland

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