Model-Based Inference of Synaptic Transmission

Ola Bykowska, Camille Gontier, Anne-Lene Sax, David W. Jia, Milton Llera Montero, Alex D. Bird, Conor Houghton, Jean-Pascal Pfister, Rui Ponte Costa
<span title="2019-08-20">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5j7w5nihpbdjxp6k7que2k24au" style="color: black;">Frontiers in Synaptic Neuroscience</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnsyn.2019.00021">doi:10.3389/fnsyn.2019.00021</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31481887">pmid:31481887</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6710341/">pmcid:PMC6710341</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gehsdzff7vdctjd7g3cg76fdne">fatcat:gehsdzff7vdctjd7g3cg76fdne</a> </span>
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