%0 Journal Article
%A Michael Lynn
%A Kevin F.H. Lee
%A Cary Soares
%A Richard Naud
%A Jean-Claude Béïque
%T A synthetic likelihood solution to the silent synapse estimation problem
%D 2019
%R 10.1101/781898
%J bioRxiv
%P 781898
%X Functional features of populations of synapses are typically inferred from random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we developed a biophysically constrained statistical framework for addressing this question and applied 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 thus simulated this method in silico and found that it is characterized by strong and systematic biases, poor reliability and weak statistical power. Key limitations were validated by whole-cell recordings from hippocampal neurons. To address these shortcomings, we employed our experimentally constrained simulations to develop a synthetic likelihood estimator that infers silent synapse fraction with no bias and low variance. Together, this generalizable implementation highlights how computational models of experimental methodologies can substantially improve inference.Significance statement The plasticity potential of neural networks is influenced by the presence of silent (AMPA-lacking) synapses. Silent synapses are more prevalent during early postnatal development, yet are found in adults where drugs of abuse are thought to regulate their occurrence. Current methods estimate this global fraction by recording from small subsets of electrically stimulated synapses. We show that a widely used failure rate-based calculation returns highly variable, positively biased estimates of silent synapse fraction. We developed an alternate estimator employing simulated likelihood functions which exhibits dramatically decreased bias and variance. This method allows the reliable interrogation of fine-grained changes in the functional make-up of synaptic populations, and exemplifies how computational models of methodologies can be adapted into statistical inference tools.
%U https://www.biorxiv.org/content/biorxiv/early/2019/11/20/781898.full.pdf