Model-based inference of synaptic transmission

Ola Bykowska, Camille Gontier, Anne-Lene Sax, David Jia, Milton Llera Montero, Alex Bird, Conor Houghton, Jean-Pascal Pfister, Rui Ponte Costa

Research output: Contribution to journalReview article (Academic Journal)

2 Citations (Scopus)
107 Downloads (Pure)

Abstract

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.
Original languageEnglish
Number of pages9
JournalFrontiers in Synaptic Neuroscience
Volume11
Issue number21
DOIs
Publication statusPublished - 20 Aug 2019

Keywords

  • quantal analysis
  • synaptic transmission
  • short-term synaptic plasticity
  • model inference
  • probabilistic inference

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