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Computational modeling of peripheral pain: a commentary

This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibili...

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Detalles Bibliográficos
Autores principales: Argüello, Erick J, Silva, Ricardo J, Huerta, Mónica K, Avila, René S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464699/
https://www.ncbi.nlm.nih.gov/pubmed/26062616
http://dx.doi.org/10.1186/s12938-015-0049-x
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author Argüello, Erick J
Silva, Ricardo J
Huerta, Mónica K
Avila, René S
author_facet Argüello, Erick J
Silva, Ricardo J
Huerta, Mónica K
Avila, René S
author_sort Argüello, Erick J
collection PubMed
description This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications.
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spelling pubmed-44646992015-06-14 Computational modeling of peripheral pain: a commentary Argüello, Erick J Silva, Ricardo J Huerta, Mónica K Avila, René S Biomed Eng Online Review This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications. BioMed Central 2015-06-11 /pmc/articles/PMC4464699/ /pubmed/26062616 http://dx.doi.org/10.1186/s12938-015-0049-x Text en © Argüello et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Review
Argüello, Erick J
Silva, Ricardo J
Huerta, Mónica K
Avila, René S
Computational modeling of peripheral pain: a commentary
title Computational modeling of peripheral pain: a commentary
title_full Computational modeling of peripheral pain: a commentary
title_fullStr Computational modeling of peripheral pain: a commentary
title_full_unstemmed Computational modeling of peripheral pain: a commentary
title_short Computational modeling of peripheral pain: a commentary
title_sort computational modeling of peripheral pain: a commentary
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464699/
https://www.ncbi.nlm.nih.gov/pubmed/26062616
http://dx.doi.org/10.1186/s12938-015-0049-x
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