NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

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...

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Bibliographic Details
Published in:PLoS computational biology 2023-03, Vol.19 (3), p.e1010941
Main Authors: Birgiolas, Justas, Haynes, Vergil, Gleeson, Padraig, Gerkin, Richard C, Dietrich, Suzanne W, Crook, Sharon
Format: Article
Language:English
Subjects:
Analysis
Automation
Biology and Life Sciences
Brain
Cell culture
Computational neuroscience
Database searching
Ecosystem
Electrophysiology
Environment models
Inspection
Interfaces
Intermediate languages
Internet resources
Internet/Web search services
Ion channels
Language
Methods
Morphology
Nervous system
Neurons
Neurons - physiology
Neurosciences
Online searching
Ontology
Physical Sciences
PubMed
Research and Analysis Methods
Searching
Simulation
Social Sciences
Software
Specification and description languages
Tooling
User interfaces
Websites
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1010941