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)


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.


Spiking Neuron Model Dynamic Synapse Supervised Learning Receptive Field Fuzzy Reasoning 


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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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