Gleeson, P. and Crook, S. and Cannon, R.C. and Hines, M.L. and Billings, G.O. and Farinella, M. and Morse, T.M. and Davison, A.P. and Ray, S. and Bhalla, U.S. and Barnes, S.R. and Dimitrova, Y.D. and Silver, R.A. and Friston, K.J. (2010) NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology , 6 (6) e1000815-1-e1000815-19. 10.1371/journal.pcbi.1000815.
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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.
|Title:||NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail|
|Open access status:||An open access publication. A version is also available from UCL Discovery.|
|Additional information:||Copyright: © 2010 Gleeson 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.|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > CoMPLEX - Maths and Physics in the Life Sciences and Experimental Biology|
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Imaging Neuroscience
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering
UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Biosciences (Division of) > Neuroscience, Physiology and Pharmacology
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