4.2 Article

A Practical Guide to Using CV Analysis for Determining the Locus of Synaptic Plasticity

期刊

FRONTIERS IN SYNAPTIC NEUROSCIENCE
卷 12, 期 -, 页码 -

出版社

FRONTIERS RESEARCH FOUNDATION
DOI: 10.3389/fnsyn.2020.00011

关键词

long-term plasticity; long-term potentiation; long-term depression; spike-timing-dependent plasticity; paired recordings; monosynaptic connections; electrophysiology

资金

  1. CFI [LOF 28331]
  2. CIHR [OG 126137, NIA 288936]
  3. FRSQ [CB 254033]
  4. NSERC [DG 418546-2, DG 2017-04730, DAS 2017-507818]
  5. McGill University Max Stern Recruitment Award
  6. McGill University Integrated Program in Neuroscience (IPN) Recruitment Award
  7. McGill University Grad Excellence Award
  8. Research Institute of the McGill University Health Centre (RI-MUHC) Studentship
  9. Healthy Brains, Healthy Lives (HBHL) Master's Fellowship

向作者/读者索取更多资源

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.

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