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Mini Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Synaptic Neurosci. | doi: 10.3389/fnsyn.2019.00021

Model-based inference of synaptic transmission

  • 1Computational Neuroscience Unit, Faculty of Engineering, University of Bristol, United Kingdom
  • 2Institut für Physiologie, Universität Bern, Switzerland
  • 3Centre for Neural Circuits and Behaviour, University of Oxford, United Kingdom
  • 4Ernst Strüngmann Institut für Neurowissenschaften, Germany

Synaptic computation is believed to underlie many forms of animal behaviour. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired recordings to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.

Keywords: Synaptic Transmission, short-term synaptic plasticity, Model inference, Probabilistic inference, quantal analysis

Received: 06 Jun 2019; Accepted: 29 Jul 2019.

Edited by:

P. Jesper Sjöström, McGill University, Canada

Reviewed by:

Ian Stevenson, University of Connecticut, United States
Matthias H. Hennig, University of Edinburgh, United Kingdom
Christian Stricker, Australian National University, Australia  

Copyright: © 2019 Bykowska, Gontier, Sax, Jia, Llera-Montero, Bird, Houghton, Pfister and Costa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Rui P. Costa, Computational Neuroscience Unit, Faculty of Engineering, University of Bristol, Bristol, England, United Kingdom,