Interoperability of Neuroscience Modeling Software: Current Status and Future Directions
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Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the “Neuro-IT Interoperability of Simulators” workshop, held at the 11th computational neuroscience meeting in Edinburgh (July 19–20 2006; http://www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance.
KeywordsNeural simulation software Simulation language Standards XML Model publication
The workshop was supported by the European Commission IST-2001-35498 Neuro-IT-Net Thematic Network. Robert Cannon was supported by NIH NIDA grant DA16454 as part of the CRCNS program. Michael Hines is funded by grants NINDS NS11613 (NEURON) and NIH DC04732 (ModelDB). Padraig Gleeson is in receipt of a Special Research Training Fellowship from the United Kingdom Medical Research Council.
- Bhalla, U. S. (2001). Modeling networks of signaling pathways. In E. De Schutter (Ed.), Computational neuroscience: Realistic modeling for experimentalists (pp. 25–48). Boca Raton: CRC.Google Scholar
- Bower, J. M., & Beeman, D. (1998). The book of Genesis: Exploring realistic neural models with the GEneral NEural SImulation System (2nd ed.). New York: Springer.Google Scholar
- Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. http://arxiv.org/abs/q-bio.NC/0611089.
- Carnevale, N. T., & Hines, M. L. (2006). The NEURON book. UK: Cambridge University Press.Google Scholar
- Crook, S., Beeman, D., Gleeson, P., & Howell, F. (2005). XML for model specification in neuroscience: An introduction and workshop summary. Brains, Minds, and Media, 1, bmm228 (urn:nbn:de:0009-3-2282).Google Scholar
- Crook, S., Gleeson, P., Howell, F., Svitak, J., & Silver, R. A. (2007). MorphML: Level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics, 5, (In press).Google Scholar
- De Schutter, E., & Beeman, D. (1998). Speeding up GENESIS simulations. In J. M. Bower & D. Beeman (Eds.), The book of GENESIS: Exploring realistic neural models with the GEneral NEural SImulation System (2nd ed., pp. 329–347). Springer New York: Telos.Google Scholar
- Diesmann, M., & Gewaltig, M.-O. (2002). NEST: An environment for neural systems simulations in Forschung und wissenschaftliches Rechnen, GWDG-Bericht (pp 43–70). In T. Plesser & V. Macho (Eds.). Göttingen (D): Ges. fuer Wissenschaftliche Datenverarbeitung.Google Scholar
- Gardner, D., Toga, A. W., Ascoli, G. A., Beatty, J., Brinkley, J. F., Dale, A. M., et al. (2003). Towards effective and rewarding data sharing. Neuroinformatics, 3, 286–289.Google Scholar
- Hille, B. (2001). Ionic channels of excitable membranes. Sunderland, MA: Sinauer Associates INC.Google Scholar
- Kötter, R., Nielse, P., Dyhrfjeld-Johnsen, J., Sommer, F. T., & Northoff, G. (2002). Multi-level neuron and network modeling in computational neuroanatomy. In G. Ascoli (Ed.), Computational neuroanatomy: Principles and methods. Totowa, NJ: Humana.Google Scholar
- Le Novere, N., Bornstein, B., Broicher, A., Courtot, M., Donizelli, M., Dharuri, H., et al. (2006). BioModels database: A free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Research, 34, D689–D691. DOI 10.1093/nar/gkj092.PubMedCrossRefGoogle Scholar
- Lloyd, W. J. (1994). Practical advantages of declarative programming. In Proc. Joint Conference on Declarative Programming, GULP-PRODE.Google Scholar
- Roth, A., Nusser, Z., & Häusser, M. (2000). Monte Carlo simulations of synaptic transmission in detailed three-dimensional reconstructions of cerebellar neuropil. European Journal of Neuroscience, 12(Suppl. 11), 14.Google Scholar
- Stiles, J. R., & Bartol, T. M. (2001). Methods for simulating realistic synaptic microphysiology using MCell. In E. De Schutter (Ed.), Computational neuroscience: Realistic modeling for experimentalists (pp. 87–127). Boca Raton: CRC.Google Scholar
- Traub, R. D., Jefferys, J. G. R., Miles, R., Whittington, M. A., & Toth, K. (1994). A branching dendritic model of a rodent CA3 pyramidal neuron. Journal of Physiology (London. Print), 481, 7995.Google Scholar