Extracting Quantal Properties of Transmission at Central Synapses - Archive ouverte HAL Access content directly
Book Sections Advanced Patch-Clamp Analysis for Neuroscientists Year : 2018

Extracting Quantal Properties of Transmission at Central Synapses


Chemical synapses enable neurons to communicate rapidly, process and filter signals and to store information. However, studying their functional properties is difficult because synaptic connections typically consist of multiple synaptic contacts that release vesicles stochastically and exhibit time-dependent behavior. Moreover, most central synapses are small and inaccessible to direct measurements. Estimation of synaptic properties from responses recorded at the soma is complicated by the presence of nonuniform release probability and nonuniform quantal properties. The presence of multivesicular release and postsynaptic receptor saturation at some synapses can also complicate the interpretation of quantal parameters. Multiple-probability fluctuation analysis (MPFA; also known as variance-mean analysis) is a method that has been developed for estimating synaptic parameters from the variance and mean amplitude of synaptic responses recorded at different release probabilities. This statistical approach, which incorporates nonuniform synaptic properties, has become widely used for studying synaptic transmission. In this chapter, we describe the statistical models used to extract quantal parameters and discuss their interpretation when applying MPFA.
Fichier principal
Vignette du fichier
emss-79416.pdf (1.18 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03367952 , version 1 (11-10-2021)



Frederic Lanore, R Angus Silver. Extracting Quantal Properties of Transmission at Central Synapses. Alon Korngreen. Advanced Patch-Clamp Analysis for Neuroscientists, 113, Springer, pp.193 - 211, 2018, Neuromethods, 978-1-4939-8043-7. ⟨10.1007/978-1-4939-3411-9_10⟩. ⟨hal-03367952⟩
9 View
121 Download



Gmail Facebook Twitter LinkedIn More