|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|
|Date Published||2020 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 Journal||Cell Rep|
|Grant List||/ / CIHR / Canada|