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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
Date Published2020 07 21

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.

Alternate JournalCell Rep
PubMed ID32697998
Grant List / / CIHR / Canada