Birgiolas, Justas;
Haynes, Vergil;
Gleeson, Padraig;
Gerkin, Richard C;
Dietrich, Suzanne W;
Crook, Sharon;
(2023)
NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML.
PLoS Computational Biology
, 19
(3)
, Article e1010941. 10.1371/journal.pcbi.1010941.
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Abstract
As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.
| Type: | Article |
|---|---|
| Title: | NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1010941 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1010941 |
| Language: | English |
| Additional information: | Copyright: © 2023 Birgiolas et al. 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 credited. |
| Keywords: | Software, Ecosystem, Neurosciences, PubMed, Neurons |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Neuro, Physiology and Pharmacology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10167194 |
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