P281 Data-driven biophysically detailed computational modeling of neuronal circuits with the NeuroML standard and software ecosystem
Ankur Sinha*1, Padraig Gleeson1, Adam Ponzi1, Subhasis Ray2, Sotirios Panagiotou3, Boris Marin4, Robin Angus Silver1
1Department of Neuroscience, Physiology and Pharmacology, University College
2TCG CREST, Kolkata, India
3Erasmus University Rotterdam, Rotterdam, Netherlands
4Universidade Federal do ABC, São Bernardo do Campo, Brazil
*Email: ankur.sinha@ucl.ac.uk
Introduction
Computational models are essential for integrating multiscale experimental data into unified theories and generating new testable hypotheses. Realistic models that include biological intricacies of neurons (morphologies, ionic conductances, subcellular processes) are critical tools for gaining a mechanistic understanding of neuronal processes. Their complexity and the disjointed landscape of software for computational neuroscience, however, makes model construction, fitting to experimental data, simulation, and re-use and dissemination a considerable challenge. Here, we present NeuroML and show that it accelerates modelling workflows and promotes FAIR (Findable, Accessible, Interoperable, Reusable) and Open computational neuroscience[1].
Methods
NeuroML provides two components: a standard and a software ecosystem. The standard is specified by a two part schema. The first part constrains the structure of NeuroML models and is used to validate model descriptions and generate libraries for programming languages. The second part consists of corresponding definitions of the dynamics of model entities in the Low Entropy Modelling Specification language[2] that allows translation of NeuroML models into simulator specific formats. The software ecosystem includes libraries and tools for building and working with NeuroML models in addition to a number of simulation engines and other NeuroML compliant tools that support different stages of the model life cycle.
Results
NeuroML is an established standardised language that provides a simulator independent model representation and accompanying ecosystem of compliant tools that support all stages of the model life cycle: creating, validating, visualising, analysing, simulating, optimising, sharing, re-using models. It provides a curated set of model building blocks for constructing new models and thus also serves as a didactic resource. We demonstrate how NeuroML supports the model life cycle by presenting a number of published NeuroML models in different species (C. elegans, rodents, humans) and different brain regions (cortex, cerebellum), highlighting their scientific contributions. We also list resources on using NeuroML and existing models.
Discussion
NeuroML is a mature standard that has evolved over years of interactions with the computational neuroscience community. The NeuroML community has strong links with simulator development communities to ensure that NeuroML remains up to date with the latest modelling requirements, and that tools remain NeuroML compliant. NeuroML also ensures that it remains extensible to cater to modelling entities that are not yet part of the standard. NeuroML also links to other neuroscience initiatives (PyNN, SONATA[3]), systems biology standards (SBML, SED-ML) and machine learning/AI formats (Model Description Format[4]) to promote interoperability. Finally, a large archive of published standardised models supports re-use of existing models.
Acknowledgements
We thank all members of the NeuroML community who have contributed to the development of the standard and the software ecosystem over the years.
References
● https://doi.org/10.7554/eLife.95135
● https://doi.org/10.3389/fninf.2014.00079
● https://doi.org/10.1371/journal.pcbi.1007696
● https://doi.org/10.1007/s10827-024-00871-5
