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        Implementing fuzzy reasoning on a spiking neural network

        Author
        Glackin, C.
        McDaid, L.
        Maguire, L.
        Sayers, H.
        Attention
        2299/12807
        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
        Publication date
        2008
        Published in
        Artificial Neural Networks - ICANN 2008
        Published version
        https://doi.org/10.1007/978-3-540-87559-8_27
        Other links
        http://hdl.handle.net/2299/12807
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