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Computer Simulation Environments

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Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 5))

Abstract

This chapter gives a brief overview of simulation tools and resources available to researchers wishing to create computational models of hippocampal function. We outline first a number of software applications which provide a range of functionality for simulating networks of neurons with varying levels of biophysical detail. We then present some ongoing initiatives designed to facilitate the development of models in a transparent and portable way across different environments. Next, we describe some of the publicly accessible databases which can be used as resources by computational modellers. Finally we provide an outlook for the field, highlighting some of the current issues facing biophysically detailed modelling and point out some of the key initiatives and sources of information for future modelling efforts.

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Further Reading

  • Ascoli, G.A., Donohue, D.E., and Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. J Neurosci 27, 9247–9251.

    Article  CAS  PubMed  Google Scholar 

  • Bhalla, U.S. and Iyengar, R. (1999). Emergent properties of networks of biological signaling pathways. Science 283, 381–387.

    Article  CAS  PubMed  Google Scholar 

  • Blackwell, K. and Hellgren-Kotaleski, J. (2002). Modelling the dynamics of second messenger pathways. In Neuroscience Databases. A Practical Guide (Boston: Kluwer Academic Publishers), pp. 63–80.

    Google Scholar 

  • Borg-Graham, L.J. (1999). Interpretations of data and mechanisms for hippocampal pyramidal cell models. In Cerebral Cortex, Volume 13: Cortical Model, P.S. Ulinski, E.G. Jones, and A. Peters, eds. (New York: Plenum Press).

    Google Scholar 

  • Borg-Graham, L.J. (2000). Additional efficient computation of branched nerve equations: adaptive time step and ideal voltage clamp. J Comput Neurosci 8, 209–226.

    Article  CAS  PubMed  Google Scholar 

  • Bower, J.M. and Beeman, D. (1997). The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System (Springer, New York).

    Google Scholar 

  • Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris, F.C., Jr., et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23, 349–398.

    Article  PubMed  Google Scholar 

  • Cannon, R., Gewaltig, M.-O., Gleeson, P., Bhalla, U., Cornelis, H., Hines, M., Howell, F., Muller, E., Stiles, J., Wils, S., and De Schutter, E. (2007). Interoperability of neuroscience modelling software: current status and future directions. Neuroinformatics 5, 127–138.

    Article  PubMed  Google Scholar 

  • Cannon, R.C., Turner, D.A., Pyapali, G.K., and Wheal, H.V. (1998). An on-line archive of reconstructed hippocampal neurons. J Neurosci Methods 84, 49–54.

    Article  CAS  PubMed  Google Scholar 

  • Carnevale, N.T. and Hines, M.L. (2006). The NEURON Book (Cambridge: Cambridge University Press).

    Google Scholar 

  • Cassara, A.M., Hagberg, G.E., Bianciardi, M., Migliore, M., and Maraviglia, B. (2008). Realistic simulations of neuronal activity: a contribution to the debate on direct detection of neuronal currents by MRI. Neuroimage 39, 87–106.

    Article  CAS  PubMed  Google Scholar 

  • Crook, S., Gleeson, P., Howell, F., Svitak, J., and Silver, R.A. (2007). MorphML: level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics 5, 96–104.

    Article  PubMed  Google Scholar 

  • Diesmann, M. and Gewaltig, M.-O. (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. and Lansner, A. (2007). Workshop report: 1st INCF workshop on large-scale modeling of the nervous system. Nat 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 (Philadelphia, PA: SIAM).

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Goddard, N.H., Hucka, M., Howell, F., Cornelis, H., Shankar, K., and Beeman, D. (2001). Towards NeuroML: model description methods for collaborative modelling in neuroscience. Philos Trans R Soc Lond B Biol Sci 356, 1209–1228.

    Article  CAS  PubMed  Google Scholar 

  • Hines, M.L. and Carnevale, N.T. (2008). Translating network models to parallel hardware in NEURON. J Neurosci Methods 169, 425–455.

    Article  CAS  PubMed  Google Scholar 

  • Kumar, A., Rotter, S., and Aertsen, A. (2008). Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci 28, 5268–5280.

    Article  CAS  PubMed  Google Scholar 

  • Migliore, M., Cannia, C., Lytton, W., Markram, H., and Hines, M. (2006). Parallel network simulations with NEURON. J Comput Neurosci 21, 119–129.

    Article  CAS  PubMed  Google Scholar 

  • Migliore, M., Ferrante, M., and Ascoli, G.A. (2005). Signal propagation in oblique dendrites of CA1 pyramidal cells. J Neurophysiol 94, 4145–4155.

    Article  PubMed  Google Scholar 

  • Morrison, A., Aertsen, A., and Diesmann, M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural Comput 19, 1437–1467.

    Article  PubMed  Google Scholar 

  • Morrison, A., Diesmann, M., and Gerstner, W. (2008). Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern 98, 459–478.

    Article  PubMed  Google Scholar 

  • Plesser, H., Eppler, J., Morrison, A., Diesmann, M., and Gewaltig, M.-O. (2007). Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers. In Euro-Par 2007 Parallel Processing, Volume 4641 of Lecture Notes in Computer Science, Berlin, Springer-Verlag, pp. 672–681.

    Google Scholar 

  • Santhakumar, V., Aradi, I., and Soltesz, I. (2005). Role of mossy fiber sprouting and mossy cell loss in hyperexcitability: a network model of the dentate gyrus incorporating cell types and axonal topography. J Neurophysiol 93, 437–453.

    Article  PubMed  Google Scholar 

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Gleeson, P., Silver, R.A., Steuber, V. (2010). Computer Simulation Environments. In: Cutsuridis, V., Graham, B., Cobb, S., Vida, I. (eds) Hippocampal Microcircuits. Springer Series in Computational Neuroscience, vol 5. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0996-1_21

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