Barri, Alessandro;
Wang, Yun;
Hansel, David;
Mongillo, Gianluigi;
(2016)
Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach.
eNeuro
, 3
(2)
, Article ENEURO.0113-15.2016. 10.1523/eneuro.0113-15.2016.
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Abstract
The dependence of the synaptic responses on the history of activation and their large variability are both distinctive features of repetitive transmission at chemical synapses. Quantitative investigations have mostly focused on trial-averaged responses to characterize dynamic aspects of the transmission—thus disregarding variability—or on the fluctuations of the responses in steady conditions to characterize variability—thus disregarding dynamics. We present a statistically principled framework to quantify the dynamics of the probability distribution of synaptic responses under arbitrary patterns of activation. This is achieved by constructing a generative model of repetitive transmission, which includes an explicit description of the sources of stochasticity present in the process. The underlying parameters are then selected via an expectation-maximization algorithm that is exact for a large class of models of synaptic transmission, so as to maximize the likelihood of the observed responses. The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.
| Type: | Article |
|---|---|
| Title: | Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1523/eneuro.0113-15.2016 |
| Publisher version: | http://dx.doi.org/10.1523/eneuro.0113-15.2016 |
| Language: | English |
| Additional information: | Copyright © 2016 Barri et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International, https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
| Keywords: | Expectation-maximization; generative modeling; quantal analysis; repetitive transmission; short-term plasticity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Neuro, Physiology and Pharmacology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10200420 |
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