Model-Based Inference of Synaptic Transmission

Bykowska, Ola; Gontier, Camille; Sax, Anne-Lene; Jia, David W.; Montero, Milton Llera; Bird, Alex D.; Houghton, Conor; Pfister, Jean-Pascal; Ponte Costa, Rui (2019). Model-Based Inference of Synaptic Transmission. Frontiers in synaptic neuroscience, 11, p. 21. Frontiers Research Foundation 10.3389/fnsyn.2019.00021

Preview

Text fnsyn-11-00021(4).pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview

Synaptic computation is believed to underlie many forms of animal behavior. 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 experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.

Interest & Impact

Downloads

151 since deposited on 12 Sep 2019
21 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item