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Evolutionary Design Of Spiking Neural Networks

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  • AMMAR BELATRECHE

    ()
    (Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, Derry, BT48 7JL, Northern Ireland)

  • LIAM P. MAGUIRE

    ()
    (Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, Derry, BT48 7JL, Northern Ireland)

  • MARTIN MCGINNITY

    (Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, Derry, BT48 7JL, Northern Ireland)

  • QING XIANG WU

    (Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Northland Road, Derry, BT48 7JL, Northern Ireland)

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    Abstract

    Unlike traditional artificial neural networks (ANNs), which use a high abstraction of real neurons, spiking neural networks (SNNs) offer a biologically plausible model of realistic neurons. They differ from classical artificial neural networks in that SNNs handle and communicate information by means of timing of individual pulses, an important feature of neuronal systems being ignored by models based on rate coding scheme. However, in order to make the most of these realistic neuronal models, good training algorithms are required. Most existing learning paradigms tune the synaptic weights in an unsupervised way using an adaptation of the famous Hebbian learning rule, which is based on the correlation between the pre- and post-synaptic neurons activity. Nonetheless, supervised learning is more appropriate when prior knowledge about the outcome of the network is available. In this paper, a new approach for supervised training is presented with a biologically plausible architecture. An adapted evolutionary strategy (ES) is used for adjusting the synaptic strengths and delays, which underlie the learning and memory processes in the nervous system. The algorithm is applied to complex non-linearly separable problems, and the results show that the network is able to perform learning successfully by means of temporal encoding of presented patterns.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

    Volume (Year): 02 (2006)
    Issue (Month): 03 ()
    Pages: 237-253

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    Handle: RePEc:wsi:nmncxx:v:02:y:2006:i:03:p:237-253

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    Related research

    Keywords: Spiking neurons; action potentials; postsynaptic potential; temporal coding; spike response model; supervised learning; evolutionary strategies;

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