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Bayesian inference of synaptic quantal parameters from correlated vesicle release
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Bird, Alex D., Wall, Mark J. and Richardson, Magnus J. E. (2016) Bayesian inference of synaptic quantal parameters from correlated vesicle release. Frontiers in Computational Neuroscience, 10 . 116. doi:10.3389/fncom.2016.00116 ISSN 1662-5188.
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Official URL: http://dx.doi.org/10.3389/fncom.2016.00116
Abstract
Synaptic transmission is both history-dependent and stochastic, resulting in varying responses to presentations of the same presynaptic stimulus. This complicates attempts to infer synaptic parameters and has led to the proposal of a number of different strategies for their quantification. Recently Bayesian approaches have been applied to make more efficient use of the data collected in paired intracellular recordings. Methods have been developed that either provide a complete model of the distribution of amplitudes for isolated responses or approximate the amplitude distributions of a train of post-synaptic potentials, with correct short-term synaptic dynamics but neglecting correlations. In both cases the methods provided significantly improved inference of model parameters as compared to existing mean-variance fitting approaches. However, for synapses with high release probability, low vesicle number or relatively low restock rate and for data in which only one or few repeats of the same pattern are available, correlations between serial events can allow for the extraction of significantly more information from experiment: a more complete Bayesian approach would take this into account also. This has not been possible previously because of the technical difficulty in calculating the likelihood of amplitudes seen in correlated post-synaptic potential trains; however, recent theoretical advances have now rendered the likelihood calculation tractable for a broad class of synaptic dynamics models. Here we present a compact mathematical form for the likelihood in terms of a matrix product and demonstrate how marginals of the posterior provide information on covariance of parameter distributions. The associated computer code for Bayesian parameter inference for a variety of models of synaptic dynamics is provided in the supplementary material allowing for quantal and dynamical parameters to be readily inferred from experimental data sets.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QP Physiology | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre |
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Library of Congress Subject Headings (LCSH): | Neural transmission, Bayesian statistical decision theory, Stochastic analysis | ||||||||
Journal or Publication Title: | Frontiers in Computational Neuroscience | ||||||||
Publisher: | Frontiers Research Foundation | ||||||||
ISSN: | 1662-5188 | ||||||||
Official Date: | 25 November 2016 | ||||||||
Dates: |
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Volume: | 10 | ||||||||
Article Number: | 116 | ||||||||
DOI: | 10.3389/fncom.2016.00116 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Description: | |||||||||
Date of first compliant deposit: | 24 November 2016 | ||||||||
Date of first compliant Open Access: | 28 November 2016 | ||||||||
Funder: | University of Warwick. Systems Biology Doctoral Training Centre, Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC) | ||||||||
Grant number: | BB/G530233/1, BB/J015369/1 (BBSRC) |
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