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TitleA Synthetic Likelihood Solution to the Silent Synapse Estimation Problem.
Publication TypeJournal Article
Year of Publication2020
AuthorsLynn, Michael B., Kevin F. H. Lee, Cary Soares, Richard Naud, and Jean-Claude Béïque
JournalCell Rep
Volume32
Issue3
Pagination107916
Date Published2020 07 21
ISSN2211-1247
Abstract

Functional features of synaptic populations are typically inferred from random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we develop a biophysically constrained statistical framework to address this question and apply it to assess the performance of a widely used method based on a failure-rate analysis to quantify the occurrence of silent (AMPAR-lacking) synapses. We simulate this method in silico and find that it is characterized by strong and systematic biases, poor reliability, and weak statistical power. Key conclusions are validated by whole-cell recordings from hippocampal neurons. To address these shortcomings, we develop a simulator of the experimental protocol and use it to compute a synthetic likelihood. By maximizing the likelihood, we infer silent synapse fraction with no bias, low variance, and superior statistical power over alternatives. Together, this generalizable approach highlights how a simulator of experimental methodologies can substantially improve the estimation of physiological properties.

DOI10.1016/j.celrep.2020.107916
Alternate JournalCell Rep
PubMed ID32697998
Grant List / / CIHR / Canada