Implementing fuzzy reasoning on a spiking neural network

C. Glackin, L. McDaid, L. Maguire, H. Sayers

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)


    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
    Original languageEnglish
    Title of host publicationArtificial Neural Networks - ICANN 2008
    Subtitle of host publicationProceedings, Part II
    PublisherSpringer Nature
    Number of pages10
    ISBN (Electronic)978-3-540-87559-8
    ISBN (Print)978-3-540-87559-8
    Publication statusPublished - 2008

    Publication series

    NameLecture Notes in Computer Science


    Dive into the research topics of 'Implementing fuzzy reasoning on a spiking neural network'. Together they form a unique fingerprint.

    Cite this