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Related Experiment Videos

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

N H Goddard1, M Hucka, F Howell

  • 1Institute for Adaptive and Neural Computation, Division of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh EH1 2QL, Scotland. nigel.goddard@ed.ac.uk

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|September 8, 2001
PubMed
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Developing computational models of complex biological nervous systems requires better software tools. NeuroML, a new markup language, uses templates for easier model description, communication, and simulation, advancing neuroscience research.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Bioinformatics

Background:

  • Biological nervous systems are highly complex, leading to intricate computational models.
  • Current computational models are difficult to define, comprehend, manage, and communicate.
  • Progress in understanding biological nervous systems necessitates improved tools for model development and exchange.

Purpose of the Study:

  • To describe methodologies and software tool requirements for neuroscientists engaged in computational modeling.
  • To introduce NeuroML, a markup language designed for neurosciences, facilitating model communication and development.
  • To propose a template hierarchy within NeuroML for describing models across various structural levels.

Main Methods:

  • Development of methodologies to support discussion, development, and exchange of computational models.

Related Experiment Videos

  • Utilizing declarative model description forms, termed templates, analogous to object-oriented classes and database schemas.
  • Defining NeuroML syntax using templates and establishing a common communication format for modeling tools.

    • Main Results:

    • Identification of key requirements for effective neuroscience modeling software tools.
    • Introduction of NeuroML, a novel markup language for neurosciences.
    • Proposal of a template hierarchy for NeuroML supporting diverse model descriptions from cell membranes to neural networks.

    Conclusions:

    • NeuroML, with its template-based structure, enhances the ability of neuroscientists to engage with computational models.
    • The proposed templates facilitate both user interaction and high-performance simulation of neural models.
    • Improved tools like NeuroML are crucial for advancing the scientific understanding of biological nervous systems.