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Towards Reproducible Descriptions of Neuronal Network Models

Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal...

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Detalles Bibliográficos
Autores principales: Nordlie, Eilen, Gewaltig, Marc-Oliver, Plesser, Hans Ekkehard
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2713426/
https://www.ncbi.nlm.nih.gov/pubmed/19662159
http://dx.doi.org/10.1371/journal.pcbi.1000456
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author Nordlie, Eilen
Gewaltig, Marc-Oliver
Plesser, Hans Ekkehard
author_facet Nordlie, Eilen
Gewaltig, Marc-Oliver
Plesser, Hans Ekkehard
author_sort Nordlie, Eilen
collection PubMed
description Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
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spelling pubmed-27134262009-08-07 Towards Reproducible Descriptions of Neuronal Network Models Nordlie, Eilen Gewaltig, Marc-Oliver Plesser, Hans Ekkehard PLoS Comput Biol Research Article Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain. Public Library of Science 2009-08-07 /pmc/articles/PMC2713426/ /pubmed/19662159 http://dx.doi.org/10.1371/journal.pcbi.1000456 Text en Nordlie 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
Nordlie, Eilen
Gewaltig, Marc-Oliver
Plesser, Hans Ekkehard
Towards Reproducible Descriptions of Neuronal Network Models
title Towards Reproducible Descriptions of Neuronal Network Models
title_full Towards Reproducible Descriptions of Neuronal Network Models
title_fullStr Towards Reproducible Descriptions of Neuronal Network Models
title_full_unstemmed Towards Reproducible Descriptions of Neuronal Network Models
title_short Towards Reproducible Descriptions of Neuronal Network Models
title_sort towards reproducible descriptions of neuronal network models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2713426/
https://www.ncbi.nlm.nih.gov/pubmed/19662159
http://dx.doi.org/10.1371/journal.pcbi.1000456
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