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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Wide-band information transmission at the calyx of Held
Authors: Yang, Zhijun
Hennig, Matthias H
Postlethwaite, Michael
Forsythe, Ian D
Graham, Bruce
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Keywords: computational neuroscience
short term synaptic dynamics
information theory
Issue Date: Apr-2009
Publisher: Massachusetts Institute of Technology Press (MIT Press)
Citation: Yang Z, Hennig MH, Postlethwaite M, Forsythe ID & Graham B (2009) Wide-band information transmission at the calyx of Held, Neural Computation, 21 (4), pp. 991-1017.
Abstract: We use a mathematical model of the calyx of Held to explore information transmission at this giant glutamatergic synapse. The significant depression of the postsynaptic response to repeated stimulation in vitro is a result of various activity-dependent processes in multiple time scales, which can be reproduced by multiexponential functions in this model. When stimulated by Poisson-distributed spike trains the amplitude of the postsynaptic current varies considerably with the preceding interspike intervals. Here we quantify the information contained in the postsynaptic current amplitude about preceding interspike intervals and determine the impact of different pre- and postsynaptic factors on information transmission. The mutual information between presynaptic spike times and the amplitude of the postsynaptic response in general decreases as the mean stimulation rate increases, but remains high even at frequencies greater than 100 Hz, unlike at many neocortical synapses. The maintenance of information transmission is attributable largely to vesicle recycling rates at low frequencies of stimulation, shifting to vesicle release probability at high frequencies. Also at higher frequencies the synapse operates largely in a release ready mode in which most release sites contain a release-ready vesicle and release probabilities are low.
Type: Journal Article
DOI Link:
Rights: Published in Neural Computation. Copyright 2008 Massachusetts Institute of Technology.; Neural Computation 21, 991–1017 (2009) © 2008 Massachusetts Institute of Technology
Affiliation: University of Stirling
Computing Science and Mathematics
University of Leicester
University of Leicester
Computing Science - CSM Dept

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