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.

Item Type:

Journal Article (Review Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Physiology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Gontier Camille, Michel Jean-Claude; Pfister, Jean Pascal and Ponte Costa, Rui André

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1663-3563

Publisher:

Frontiers Research Foundation

Language:

English

Submitter:

Camille Gontier

Date Deposited:

12 Sep 2019 11:16

Last Modified:

24 Oct 2019 04:33

Publisher DOI:

10.3389/fnsyn.2019.00021

PubMed ID:

31481887

BORIS DOI:

10.7892/boris.133073

URI:

https://boris.unibe.ch/id/eprint/133073

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