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    Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity-strengthening correlated input pathways

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    Author
    Gilson, M; Burkitt, AN; Grayden, DB; Thomas, DA; van Hemmen, JL
    Date
    2009-08-01
    Source Title
    BIOLOGICAL CYBERNETICS
    Publisher
    SPRINGER
    University of Melbourne Author/s
    Burkitt, Anthony; Grayden, David; Thomas, Doreen; GILSON, MATTHIEU
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Gilson, M., Burkitt, A. N., Grayden, D. B., Thomas, D. A. & van Hemmen, J. L. (2009). Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity-strengthening correlated input pathways. BIOLOGICAL CYBERNETICS, 101 (2), pp.81-102. https://doi.org/10.1007/s00422-009-0319-4.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29257
    DOI
    10.1007/s00422-009-0319-4
    Abstract
    Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity. In this paper, we extend previous studies of input selectivity induced by (STDP) for single neurons to the biologically interesting case of a neuronal network with fixed recurrent connections and plastic connections from external pools of input neurons. We use a theoretical framework based on the Poisson neuron model to analytically describe the network dynamics (firing rates and spike-time correlations) and thus the evolution of the synaptic weights. This framework incorporates the time course of the post-synaptic potentials and synaptic delays. Our analysis focuses on the asymptotic states of a network stimulated by two homogeneous pools of "steady" inputs, namely Poisson spike trains which have fixed firing rates and spike-time correlations. The (STDP) model extends rate-based learning in that it can implement, at the same time, both a stabilization of the individual neuron firing rates and a slower weight specialization depending on the input spike-time correlations. When one input pathway has stronger within-pool correlations, the resulting synaptic dynamics induced by (STDP) are shown to be similar to those arising in the case of a purely feed-forward network: the weights from the more correlated inputs are potentiated at the expense of the remaining input connections.
    Keywords
    Artificial Intelligence and Image Processing

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