Literature DB >> 17873371

MorphML: level 1 of the NeuroML standards for neuronal morphology data and model specification.

Sharon Crook1, Padraig Gleeson, Fred Howell, Joseph Svitak, R Angus Silver.   

Abstract

Quantitative neuroanatomical data are important for the study of many areas of neuroscience, and the complexity of problems associated with neuronal structure requires that research from multiple groups across many disciplines be combined. However, existing neuron-tracing systems, simulation environments, and tools for the visualization and analysis of neuronal morphology data use a variety of data formats, making it difficult to exchange data in a readily usable way. The NeuroML project was initiated to address these issues, and here we describe an extensible markup language standard, MorphML, which defines a common data format for neuronal morphology data and associated metadata to facilitate data and model exchange, database creation, model publication, and data archiving. We describe the elements of the standard in detail and outline the mappings between this format and those used by a number of popular applications for reconstruction, simulation, and visualization of neuronal morphology.

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Year:  2007        PMID: 17873371      PMCID: PMC6130779          DOI: 10.1007/s12021-007-0003-6

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  21 in total

Review 1.  Towards NeuroML: model description methods for collaborative modelling in neuroscience.

Authors:  N H Goddard; M Hucka; F Howell; H Cornelis; K Shankar; D Beeman
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2001-08-29       Impact factor: 6.237

2.  Propagation of action potentials in dendrites depends on dendritic morphology.

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3.  ModelDB: A Database to Support Computational Neuroscience.

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Journal:  J Comput Neurosci       Date:  2004 Jul-Aug       Impact factor: 1.621

4.  Towards effective and rewarding data sharing.

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Journal:  Neuroinformatics       Date:  2003

5.  Effects of variability in anatomical reconstruction techniques on models of synaptic integration by dendrites: a comparison of three Internet archives.

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Journal:  Eur J Neurosci       Date:  2004-03       Impact factor: 3.386

6.  A cross-platform freeware tool for digital reconstruction of neuronal arborizations from image stacks.

Authors:  Kerry M Brown; Duncan E Donohue; Giampaolo D'Alessandro; Giorgio A Ascoli
Journal:  Neuroinformatics       Date:  2005

7.  A general XML schema and SPM toolbox for storage of neuro-imaging results and anatomical labels.

Authors:  David Bryant Keator; Syam Gadde; Jeffrey S Grethe; Derek V Taylor; Steven G Potkin
Journal:  Neuroinformatics       Date:  2006

8.  Fitting experimental data to models that use morphological data from public databases.

Authors:  W R Holmes; J Ambros-Ingerson; L M Grover
Journal:  J Comput Neurosci       Date:  2006-04-22       Impact factor: 1.621

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10.  Influence of dendritic structure on firing pattern in model neocortical neurons.

Authors:  Z F Mainen; T J Sejnowski
Journal:  Nature       Date:  1996-07-25       Impact factor: 49.962

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  25 in total

Review 1.  Successes and rewards in sharing digital reconstructions of neuronal morphology.

Authors:  Giorgio A Ascoli
Journal:  Neuroinformatics       Date:  2007

2.  Neuroinformatics grand challenges.

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4.  The Filament Editor: an interactive software environment for visualization, proof-editing and analysis of 3D neuron morphology.

Authors:  Vincent J Dercksen; Hans-Christian Hege; Marcel Oberlaender
Journal:  Neuroinformatics       Date:  2014-04

5.  The International Neuroinformatics Coordinating Facility: evaluating the first years.

Authors:  Erik De Schutter
Journal:  Neuroinformatics       Date:  2009-07-28

6.  High-accuracy neurite reconstruction for high-throughput neuroanatomy.

Authors:  Moritz Helmstaedter; Kevin L Briggman; Winfried Denk
Journal:  Nat Neurosci       Date:  2011-07-10       Impact factor: 24.884

Review 7.  Is realistic neuronal modeling realistic?

Authors:  Mara Almog; Alon Korngreen
Journal:  J Neurophysiol       Date:  2016-08-17       Impact factor: 2.714

8.  Terminology for neuroscience data discovery: multi-tree syntax and investigator-derived semantics.

Authors:  Daniel Gardner; David H Goldberg; Bernice Grafstein; Adrian Robert; Esther P Gardner
Journal:  Neuroinformatics       Date:  2008-10-29

9.  Trends in programming languages for neuroscience simulations.

Authors:  Andrew P Davison; Michael L Hines; Eilif Muller
Journal:  Front Neurosci       Date:  2009-12-15       Impact factor: 4.677

10.  PyMOOSE: Interoperable Scripting in Python for MOOSE.

Authors:  Subhasis Ray; Upinder S Bhalla
Journal:  Front Neuroinform       Date:  2008-12-19       Impact factor: 4.081

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