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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Autores principales:	Eilen Nordlie, Marc-Oliver Gewaltig, Hans Ekkehard Plesser
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/08cb1fe7da604dcab6c2d6668134bc5b
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spelling	oai:doaj.org-article:08cb1fe7da604dcab6c2d6668134bc5b2021-11-25T05:42:15ZTowards reproducible descriptions of neuronal network models.1553-734X1553-735810.1371/journal.pcbi.1000456https://doaj.org/article/08cb1fe7da604dcab6c2d6668134bc5b2009-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19662159/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Progress 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.Eilen NordlieMarc-Oliver GewaltigHans Ekkehard PlesserPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 8, p e1000456 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Eilen Nordlie Marc-Oliver Gewaltig Hans Ekkehard Plesser Towards reproducible descriptions of neuronal network models.
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.
format	article
author	Eilen Nordlie Marc-Oliver Gewaltig Hans Ekkehard Plesser
author_facet	Eilen Nordlie Marc-Oliver Gewaltig Hans Ekkehard Plesser
author_sort	Eilen Nordlie
title	Towards reproducible descriptions of neuronal network models.
title_short	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_sort	towards reproducible descriptions of neuronal network models.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/08cb1fe7da604dcab6c2d6668134bc5b
work_keys_str_mv	AT eilennordlie towardsreproducibledescriptionsofneuronalnetworkmodels AT marcolivergewaltig towardsreproducibledescriptionsofneuronalnetworkmodels AT hansekkehardplesser towardsreproducibledescriptionsofneuronalnetworkmodels
_version_	1718414500411998208

Towards reproducible descriptions of neuronal network models.

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