Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package

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 ru...

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Detalles Bibliográficos
Autores principales: Lynn, Michael, Naud, Richard, Béïque, Jean-Claude
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757407/
https://www.ncbi.nlm.nih.gov/pubmed/33377070
http://dx.doi.org/10.1016/j.xpro.2020.100176
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author Lynn, Michael
Naud, Richard
Béïque, Jean-Claude
author_facet Lynn, Michael
Naud, Richard
Béïque, Jean-Claude
author_sort Lynn, Michael
collection PubMed
description 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).
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spelling pubmed-77574072020-12-28 Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package Lynn, Michael Naud, Richard Béïque, Jean-Claude STAR Protoc Protocol 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). Elsevier 2020-11-24 /pmc/articles/PMC7757407/ /pubmed/33377070 http://dx.doi.org/10.1016/j.xpro.2020.100176 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Lynn, Michael
Naud, Richard
Béïque, Jean-Claude
Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
title Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
title_full Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
title_fullStr Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
title_full_unstemmed Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
title_short Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
title_sort accurate silent synapse estimation from simulator-corrected electrophysiological data using the silentmle python package
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757407/
https://www.ncbi.nlm.nih.gov/pubmed/33377070
http://dx.doi.org/10.1016/j.xpro.2020.100176
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