Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization...

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Main Authors:	Pozzorini, Christian, Mensi, Skander, Hagens, Olivier, Naud, Richard, Koch, Christof, Gerstner, Wulfram
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470831/

id	pubmed-4470831
recordtype	oai_dc
spelling	pubmed-44708312015-06-29 Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models Pozzorini, Christian Mensi, Skander Hagens, Olivier Naud, Richard Koch, Christof Gerstner, Wulfram Research Article Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons. Public Library of Science 2015-06-17 /pmc/articles/PMC4470831/ /pubmed/26083597 http://dx.doi.org/10.1371/journal.pcbi.1004275 Text en © 2015 Pozzorini et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Pozzorini, Christian Mensi, Skander Hagens, Olivier Naud, Richard Koch, Christof Gerstner, Wulfram
spellingShingle	Pozzorini, Christian Mensi, Skander Hagens, Olivier Naud, Richard Koch, Christof Gerstner, Wulfram Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
author_facet	Pozzorini, Christian Mensi, Skander Hagens, Olivier Naud, Richard Koch, Christof Gerstner, Wulfram
author_sort	Pozzorini, Christian
title	Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
title_short	Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
title_full	Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
title_fullStr	Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
title_full_unstemmed	Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
title_sort	automated high-throughput characterization of single neurons by means of simplified spiking models
description	Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470831/
_version_	1613236806627622912

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

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