Title | A Synthetic Likelihood Solution to the Silent Synapse Estimation Problem. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Lynn, Michael B., Kevin F. H. Lee, Cary Soares, Richard Naud, and Jean-Claude Béïque |
Journal | Cell Rep |
Volume | 32 |
Issue | 3 |
Pagination | 107916 |
Date Published | 2020 07 21 |
ISSN | 2211-1247 |
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. |
DOI | 10.1016/j.celrep.2020.107916 |
Alternate Journal | Cell Rep |
PubMed ID | 32697998 |
Grant List | / / CIHR / Canada |