A Practical Guide to Using CV Analysis for Determining the Locus of Synaptic Plasticity.
electrophysiology
long-term depression
long-term plasticity
long-term potentiation
monosynaptic connections
paired recordings
spike-timing-dependent plasticity
Journal
Frontiers in synaptic neuroscience
ISSN: 1663-3563
Titre abrégé: Front Synaptic Neurosci
Pays: Switzerland
ID NLM: 101548972
Informations de publication
Date de publication:
2020
2020
Historique:
received:
02
12
2019
accepted:
04
03
2020
entrez:
16
4
2020
pubmed:
16
4
2020
medline:
16
4
2020
Statut:
epublish
Résumé
Long-term synaptic plasticity is widely believed to underlie learning and memory in the brain. Whether plasticity is primarily expressed pre- or postsynaptically has been the subject of considerable debate for many decades. More recently, it is generally agreed that the locus of plasticity depends on a number of factors, such as developmental stage, induction protocol, and synapse type. Since presynaptic expression alters not just the gain but also the short-term dynamics of a synapse, whereas postsynaptic expression only modifies the gain, the locus has fundamental implications for circuits dynamics and computations in the brain. It therefore remains crucial for our understanding of neuronal circuits to know the locus of expression of long-term plasticity. One classical method for elucidating whether plasticity is pre- or postsynaptically expressed is based on analysis of the coefficient of variation (CV), which serves as a measure of noise levels of synaptic neurotransmission. Here, we provide a practical guide to using CV analysis for the purposes of exploring the locus of expression of long-term plasticity, primarily aimed at beginners in the field. We provide relatively simple intuitive background to an otherwise theoretically complex approach as well as simple mathematical derivations for key parametric relationships. We list important pitfalls of the method, accompanied by accessible computer simulations to better illustrate the problems (downloadable from GitHub), and we provide straightforward solutions for these issues.
Identifiants
pubmed: 32292337
doi: 10.3389/fnsyn.2020.00011
pmc: PMC7118219
doi:
Types de publication
Journal Article
Langues
eng
Pagination
11Informations de copyright
Copyright © 2020 Brock, Thomazeau, Watanabe, Li and Sjöström.
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