Spike-timing-dependent plasticity for neurons with recurrent connections

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    Author
    Burkitt, AN; Gilson, M; van Hemmen, JL
    Date
    2007-05-01
    Source Title
    Biological Cybernetics: communication and control in organisms and automata
    Publisher
    SPRINGER
    University of Melbourne Author/s
    Burkitt, Anthony; GILSON, MATTHIEU
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Burkitt, A. N., Gilson, M. & van Hemmen, J. L. (2007). Spike-timing-dependent plasticity for neurons with recurrent connections. BIOLOGICAL CYBERNETICS, 96 (5), pp.533-546. https://doi.org/10.1007/s00422-007-0148-2.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29256
    DOI
    10.1007/s00422-007-0148-2
    Abstract
    The dynamics of the learning equation, which describes the evolution of the synaptic weights, is derived in the situation where the network contains recurrent connections. The derivation is carried out for the Poisson neuron model. The spiking-rates of the recurrently connected neurons and their cross-correlations are determined self- consistently as a function of the external synaptic inputs. The solution of the learning equation is illustrated by the analysis of the particular case in which there is no external synaptic input. The general learning equation and the fixed-point structure of its solutions is discussed.
    Keywords
    Artificial Intelligence and Image Processing

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