Skip to main content

NeuroML

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.

Keywords

  • Neuronal Morphology
  • Computational Neuroscience
  • Spike Timing Dependent Plasticity
  • Neuroscience Information Framework
  • Synapse Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-94-007-3858-4_16
  • Chapter length: 29 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   149.00
Price excludes VAT (USA)
  • ISBN: 978-94-007-3858-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   199.99
Price excludes VAT (USA)
Hardcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 16.1
Fig. 16.2
Fig. 16.3
Fig. 16.4
Fig. 16.5
Fig. 16.6
Fig. 16.7
Fig. 16.8
Fig. 16.9
Fig. 16.10
Fig. 16.11

Notes

  1. 1.

    http://www.neuroml.org

  2. 2.

    http://symphony.bu.edu

  3. 3.

    http://www.virtualratbrain.org

  4. 4.

    http://sourceforge.net/projects/neuroml

  5. 5.

    http://www.brainml.org

  6. 6.

    http://www.nineml.org

  7. 7.

    http://www.neuinfo.org

  8. 8.

    http://www.ebi.ac.uk/sbo

  9. 9.

    http://www.geneontology.org

  10. 10.

    http://www.neuroml.org/specifications

  11. 11.

    for example see http://neuroml.org/NeuroMLValidator/Latest.jsp?spec=MorphML

  12. 12.

    http://neuroml.svn.sourceforge.net/viewvc/neuroml

  13. 13.

    http://www.web3d.org/x3d

  14. 14.

    http://www.neuroml.org/neuron_tools

  15. 15.

    http://www.neuroml.org/neuron_tools

  16. 16.

    see http://www.neuroConstruct.org/docs/importneuron for more details

  17. 17.

    http://www.hdfgroup.org/products/hdf5_tools

  18. 18.

    http://www.neuroml.org/neuron_tools

  19. 19.

    http://www.lsm.tugraz.at/pcsim

References

  • Ascoli GA, Donohue DE, Halavi M (2007) Neuro{M}orpho.org: a central resource for neuronal morphologies. J Neurosci 27(35):9247–9251

    Google Scholar 

  • Bower J, Beeman D (1997) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation system. Springer, New York

    Google Scholar 

  • Bray T, Paoli J, Sperberg-McQueen CM (1998) Extensible Markup Language (XML) 1.0. Http://www.w3.org/TR/REC-xml

    Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • Carnevale NT, Hines ML (2006) The NEURON book. Cambridge University Press, Cambridge

    CrossRef  Google Scholar 

  • Cornelis H, De Schutter E (2003) NeuroSpaces: separating modeling and simulation. Neurocomputing 52(4):227–231

    CrossRef  Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • 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

    Google Scholar 

  • De Schutter E (2008) Why are computational neuroscience and systems biology so separate? PLoS Comput Biol 4(5):e1000078

    PubMed  CrossRef  Google Scholar 

  • 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

    Google Scholar 

  • 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

    CrossRef  Google Scholar 

  • Gardner D (2004) Neurodatabase.org: networking the microelectrode. Nat Neurosci 7(5):486–487

    Google Scholar 

  • 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

    PubMed  CrossRef  CAS  Google Scholar 

  • Gleeson P, Steuber V, Silver RA (2007) neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54(2):219–235

    PubMed  CrossRef  CAS  Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • 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

    PubMed  CrossRef  CAS  Google Scholar 

  • Goodman D, Brette R (2008) Brian: a simulator for spiking neural networks in Python. Front Neuroinformatics 2:5

    Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • 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

    CrossRef  Google Scholar 

  • 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

    PubMed  CrossRef  CAS  Google Scholar 

  • Lloyd CM, Halstead MD, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85(2–3):433–450

    PubMed  CrossRef  CAS  Google Scholar 

  • Qi W, Crook S (2004) Tools for neuroinformatic data exchange: an XML application for neuronal morphology data. Neurocomputing 58–60:1091–1095

    CrossRef  Google Scholar 

  • Ray S, Bhalla US (2008) PyMOOSE: interoperable scripting in Python for MOOSE. Front Neuroinformatics (2)

    Google Scholar 

  • 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

    PubMed  CrossRef  CAS  Google Scholar 

  • Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3:919–926

    PubMed  CrossRef  CAS  Google Scholar 

  • 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

    PubMed  CrossRef  Google Scholar 

  • 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

    PubMed  CrossRef  CAS  Google Scholar 

  • Tsodyks M, Uziel A, Markram H (2000) Synchrony generation in recurrent networks with frequency-dependent synapses. J Neurosci 20:RC50

    Google Scholar 

Download references

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??

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Padraig Gleeson .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

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

Download citation