| Title | Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package. |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Lynn, Michael, Richard Naud, and Jean-Claude Béïque |
| Journal | STAR Protoc |
| Volume | 1 |
| Issue | 3 |
| Pagination | 100176 |
| Date Published | 2020 Dec 18 |
| ISSN | 2666-1667 |
| Abstract | The proportion of silent (AMPAR-lacking) synapses is thought to be related to the plasticity potential of neural networks. We created a maximum-likelihood estimator of silent synapse fraction based on simulations of the underlying experimental methodology. Here, we provide a set of guidelines for running a Python package on compatible experimental synaptic data. Compared with traditional failure-rate approaches, this synthetic likelihood estimator improves the validity and accuracy of the estimates of the silent synapse fraction. For complete details on the use and execution of this protocol, please refer to Lynn et al. (2020). |
| DOI | 10.1016/j.xpro.2020.100176 |
| Alternate Journal | STAR Protoc |
| PubMed ID | 33377070 |
| PubMed Central ID | PMC7757407 |