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    Analysis of integrate and fire neurons: synchronization of synaptic input and spike output

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    Analysis of integrate and fire neurons: synchronization of synaptic input and spike output (402.1Kb)

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    Author
    Burkitt, A. N.; Clark, Graeme M.
    Date
    1999
    Source Title
    Neural Computation
    University of Melbourne Author/s
    Clark, Graeme; Burkitt, Anthony
    Metadata
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    Document Type
    Journal Article
    Citations
    Burkitt, A. N., & Clark, G. M. (1999). Analysis of integrate and fire neurons: synchronization of synaptic input and spike output. Neural Computation, 11(4), 871-901.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/27524
    Description

    A. N. Burkitt and G. M. Clark, 'Analysis of Integrate and Fire Neurons: Synchronization of Synaptic Input and Spike Output', Neural Computation, 11:4 (May 15, 1999), pp. 871-901. © 1999 by the Massachusetts Institute of Technology. http://www.mitpressjournals.org.ezp.lib.unimelb.edu.au/toc/neco/11/4

    Abstract
    A new technique for analysing the probability distribution of output spikes for the integrate-and-fire model is presented. This technique enables us to investigate models with arbitrary synaptic response functions that incorporate both leakage across the membrane and a rise time of the postsynaptic potential. The results, which are compared with numerical simulations, are exact in the limit of a large number of small-amplitude inputs. This method is applied to the synchronization problem, in which we examine the relationship between the spread in arrival times of the inputs (the temporal jitter of the synaptic input) and the resultant spread in the times at which the output spikes are generated (output jitter). The results of previous studies, which indicated that the ratio of the output jitter to the input jitter is consistently less than one and that it decreases for increasing numbers of inputs, are confirmed for three classes of the integrate-and-fire model. In addition to the previously identified factors of axonal propagation times and synaptic jitter, we identify the variation in the number of active inputs as being important factors that determine the timing jitter in layered networks. Previously observed phase differences between optimally and suboptimally stimulated neurons may be understood in terms of the relative time taken to reach threshold.
    Keywords
    otolaryngology; cochlear implant; integrate-and-fire model

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