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

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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.

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