NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

By Padraig Gleeson, S. Crook, R. Cannon, Michael L. Hines, Guy O. Billings, Matteo Farinella, Thomas M. Morse, Andrew P. Davison, Subhasis Ray, U. Bhalla and 3 more. PLoS Comput. Biol., 2010

334 citations recorded by the source corpus.

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

Subfields: Biophysics, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b1f26240e0050e42eb29219b9e3ce9cfe4c38bdb

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Citations in the corpus

TrakEM2 Software for Neural Circuit Reconstruction (2012, methodology): “The resulting neuronal circuit is then exported in various formats including NeuroML [11].”
Controlled vocabularies and semantics in systems biology (2011, methodology): “Computational models, expressed in representation formats such as the Systems Biology Markup Language (SBML; Hucka et al , 2003), CellML (Lloyd et al , 2004) and NeuroML (Gleeson et al , 2010), still require much human interpretation.”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, methodology): “Cell and synaptic models in NeuroML2/LEMS format (Gleeson et al., 2010) and links to the simulation and data management code are available on the Open Source Brain (http://www.opensourcebrain.org/projects/ granule-cell-layer-piasini-2014).”
OpenWorm: an open-science approach to modeling Caenorhabditis elegans (2014, methodology): “The NeuroML language (Gleeson et al., 2010) was used to encode the multi-compartmental structure of each of the 302 neurons, along with information on the locations and types of chemical and electrical synapses known to exist.”
Neuromorphic computing at scale (2025, neutral): “AI vision toolkits, simulators/analytical tools and automatic-hyperparameter-search frameworks are integrated into existing toolchains 40,50,52,53,59–72 .”
Brian 2: an intuitive and efficient neural simulator (2019, background): “Another approach to model definitions has been established by the development of simulator-independent markup languages, for example NeuroML/LEMS (Gleeson et al., 2010; Cannon et al., 2014) and NineML (Raikov et al., 2011).”