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A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data
A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a stat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894976/ https://www.ncbi.nlm.nih.gov/pubmed/24465520 http://dx.doi.org/10.1371/journal.pone.0085269 |
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author | Meng, Liang Kramer, Mark A. Middleton, Steven J. Whittington, Miles A. Eden, Uri T. |
author_facet | Meng, Liang Kramer, Mark A. Middleton, Steven J. Whittington, Miles A. Eden, Uri T. |
author_sort | Meng, Liang |
collection | PubMed |
description | A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data. |
format | Online Article Text |
id | pubmed-3894976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38949762014-01-24 A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data Meng, Liang Kramer, Mark A. Middleton, Steven J. Whittington, Miles A. Eden, Uri T. PLoS One Research Article A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data. Public Library of Science 2014-01-17 /pmc/articles/PMC3894976/ /pubmed/24465520 http://dx.doi.org/10.1371/journal.pone.0085269 Text en © 2014 Meng 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. |
spellingShingle | Research Article Meng, Liang Kramer, Mark A. Middleton, Steven J. Whittington, Miles A. Eden, Uri T. A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data |
title | A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data |
title_full | A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data |
title_fullStr | A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data |
title_full_unstemmed | A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data |
title_short | A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data |
title_sort | unified approach to linking experimental, statistical and computational analysis of spike train data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894976/ https://www.ncbi.nlm.nih.gov/pubmed/24465520 http://dx.doi.org/10.1371/journal.pone.0085269 |
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