Abstract
NeuroML is a language based on XML for describing detailed neuronal models, which can contain multiple active conductances and complex morphologies. Networks of such cells positioned and synaptically connected in 3D can also be described. In this chapter we present an overview of the history of NeuroML, a brief description of the current version of the language, plans for future developments and the relationship to other standardisation initiatives in the wider computational neuroscience field. We also present a list of NeuroML resources which are currently available, such as language specifications, services on the NeuroML website, examples of models in this format, simulation platform support, and other applications for generating and visualising highly detailed neuronal networks. These resources illustrate how NeuroML can be a key part of the toolchain for researchers addressing complex questions of neuronal system function.
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for example see http://neuroml.org/NeuroMLValidator/Latest.jsp?spec=MorphML
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see http://www.neuroConstruct.org/docs/importneuron for more details
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References
Ascoli GA, Donohue DE, Halavi M (2007) Neuro{M}orpho.org: a central resource for neuronal morphologies. J Neurosci 27(35):9247–9251
Bower J, Beeman D (1997) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation system. Springer, New York
Bray T, Paoli J, Sperberg-McQueen CM (1998) Extensible Markup Language (XML) 1.0. Http://www.w3.org/TR/REC-xml
Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris JFC, Zirpe M, Natschlager T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23(3):349–398
Cannon R, Gewaltig MO, Gleeson P, Bhalla U, Cornelis H, Hines M, Howell F, Muller E, Stiles J, Wils S, De Schutter E (2007) Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics 5(2):127–138
Cannon RC, O’Donnell C, Nolan MF (2010) Stochastic ion channel gating in dendritic neurons: morphology dependence and probabilistic synaptic activation of dendritic spikes. PLoS Comput Biol 6(8):e1000886
Carnevale NT, Hines ML (2006) The NEURON book. Cambridge University Press, Cambridge
Cornelis H, De Schutter E (2003) NeuroSpaces: separating modeling and simulation. Neurocomputing 52(4):227–231
Crook S, Gleeson P, Howell F, Svitak J, Silver RA (2007) MorphML: Level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics 5(2):96–104
Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P (2008) PyNN: a common interface for neuronal network simulators. Front Neuroinf 2:11
De Schutter E (2008) Why are computational neuroscience and systems biology so separate? PLoS Comput Biol 4(5):e1000078
Diesmann M, Gewaltig MO (2002) NEST: An Environment for Neural Systems Simulations, vol Forschung und wisschenschaftliches Rechnen, Beitrage zum Heinz-Billing-Preis 2001. Gottingen: Ges. fur Wiss. Datenverarbeitung
Djurfeldt M, Lansner A (2007) Workshop report: 1st INCF workshop on large-scale modeling of the nervous system. Nature precedings http://dx.doi.org/10.1038/npre.2007.262.1
Ermentrout B (2002) Simulating, analyzing, and animating dynamical systems: a guide to XPPAUT for researchers and students. Society for Industrial and Applied Mathematics, Philadelphia
Gardner D (2004) Neurodatabase.org: networking the microelectrode. Nat Neurosci 7(5):486–487
Gardner D, Knuth KH, Abato M, Erde SM, White T, DeBellis R, Gardner EP (2001) Common data model for neuroscience data and data model exchange. J Am Med Inform Assoc 8(1):17–33
Gleeson P, Steuber V, Silver RA (2007) neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54(2):219–235
Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, Farinella M, Morse TM, Davison AP, Ray S, Bhalla US, Barnes SR, Dimitrova YD, Silver RA (2010) NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol 6(6):e1000815
Goddard NH, Hucka M, Howell F, Cornelis H, Shankar K, Beeman D (2001) Towards NeuroML: model description methods for collaborative modelling in neuroscience. Philos Trans R Soc Lond B Biol Sci 356(1412):1209–1228
Goodman D, Brette R (2008) Brian: a simulator for spiking neural networks in Python. Front Neuroinformatics 2:5
Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM (2004) ModelDB: a database to support computational neuroscience. J Comput Neurosci 17(1):7–11
Howell F, Cannon R, Goddard N, Bringmann H, Rogister P, Cornelis H (2003) Linking computational neuroscience simulation tools–a pragmatic approach to component-based development. Neurocomputing 52–54:289–294
Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin I, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J (2003) The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531
Lloyd CM, Halstead MD, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85(2–3):433–450
Qi W, Crook S (2004) Tools for neuroinformatic data exchange: an XML application for neuronal morphology data. Neurocomputing 58–60:1091–1095
Ray S, Bhalla US (2008) PyMOOSE: interoperable scripting in Python for MOOSE. Front Neuroinformatics (2)
Rhodes PA, Llinas RR (2001) Apical tuft input efficacy in layer 5 pyramidal cells from rat visual cortex. J Physiol 536(1):167–187. DOI 10.1111/j.1469-7793.2001.00167.x
Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3:919–926
Traub RD, Contreras D, Cunningham MO, Murray H, LeBeau FE, Roopun A, Bibbig A, Wilent WB, Higley MJ, Whittington MA (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93(4):2194–2232
Tsodyks MV, Markram H (1997) The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc Natl Acad Sci USA 94:719–723
Tsodyks M, Uziel A, Markram H (2000) Synchrony generation in recurrent networks with frequency-dependent synapses. J Neurosci 20:RC50
Acknowledgements
The NeuroML initiative has involved contributions from a large number of researchers over many years. Please see http://www.neuroml.org/contributors.php for full details on contributors. Support for PG and RAS came from the MRC (Program grant G0400598 to RAS and a Special Research Training Fellowship to PG), the BBSRC (005490), the EU (EUSynapse, LSHM-CT-2005-019055) and the Welcome Trust (086699 to RAS). RAS is in receipt of a Wellcome Senior Research Fellowship (064413). Contributions of SC were supported by NIH R01MH081905. Volker??
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Gleeson, P., Steuber, V., Silver, R.A., Crook, S. (2012). NeuroML. In: Le Novère, N. (eds) Computational Systems Neurobiology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-3858-4_16
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