UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Gleeson, P; Crook, S; Cannon, RC; Hines, ML; Billings, GO; Farinella, M; Morse, TM; ... Silver, RA; + view all (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) , Article e1000815. 10.1371/journal.pcbi.1000815. Green open access

This is the latest version of this eprint.

[img]
Preview
PDF
142390.pdf

Download (2MB)

Abstract

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.

Type: Article
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 version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1000815
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1000815
Language: English
Additional information: © 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.
Keywords: GAMMA-OSCILLATIONS, SYNAPTIC PLASTICITY, SYSTEMS BIOLOGY, NEUROSCIENCE, CELL, SIMULATION, GENERATION, PROPAGATION, INHIBITION, MECHANISMS
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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.ucl.ac.uk/id/eprint/142390

Available Versions of this Item

Downloads since deposit
167Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item