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Parallel network simulations with NEURON

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Abstract

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.

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References

  • Almási G, Heidelberger P, Archer CJ, Martorell X, Erway CC, Moreira JE, Steinmacher-Burow B, Zheng Y (2005) Optimization of MPI collective communication on BlueGene/L systems, Proc. 19th annual international conference on Supercomputing, Cambridge MA, pp. 253–262.

  • Bush PC, Prince DA, Miller KD (1999) Increased pyramidal excitability and NMDA conductance can explain posttraumatic epileptogenesis without disinhibition a model. J. Neurophysiol. 82:1748–1758.

    PubMed  CAS  Google Scholar 

  • Carriero N, Gelernter D (1989) Linda in context. Communications of the ACM, April 1989.

  • Delorme A, Thorpe SJ (2003) SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons. Network 14: 613–627.

    Article  PubMed  Google Scholar 

  • Davison AP, Feng J, Brown D (2003) Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model. J. Neurophysiol 90: 1921–1935.

    Article  PubMed  CAS  Google Scholar 

  • Goddard NH, Hood G (1998) Large-scale simulation using parallel GENESIS. In: JM Bower, D Beeman eds. The Book of GENESIS, 2nd edn. Springer-Verlag.

  • Goddard N, Hood G, Howell F, Hines M, De Schutter E (2001) NEOSIM: Portable large-scale plug and play modelling. Neurocomputing 38–40: 1657–1661

    Article  Google Scholar 

  • Hammarlund P, Ekeberg Ö, Wilhelmsson T, Lansner A (1996): Large neural network simulations on multiple hardware platforms. In: JM Bower (ed), The Neurobiology of Computation, Boston.

    Google Scholar 

  • Hines ML, Carnevale T (1997) The NEURON simulation environment. Neural Comp. 9: 178–1209.

    Article  Google Scholar 

  • Hines ML, Carnevale NT (2004) Discrete event simulation in the NEURON environment. Neurocomputing 58–60: 1117–1122.

    Article  Google Scholar 

  • Hindmarsh A, Serban R (2002) User documentation for CVODES, an ODE solver with sensitivity analysis capabilities.Tech. rep., Lawrence Livermore National Laboratory. http://www.llnl.gov/ CASC/sundials/.

  • Howell FW, Dyrhfjeld-Johnsen J, Maex R, Goddard N, De Schutter E (2000) A large scale model of the cerebellar cortex using PGENESIS. Neurocomuting 32: 1041–1046.

    Article  Google Scholar 

  • Karypis G, Kumar V (1998) Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Comput. 48(1): 96–129.

    Article  Google Scholar 

  • Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput. 17: 903–921.

    Article  PubMed  Google Scholar 

  • Markram H (2006) The Blue Brain project, Nature Rev. Neurosci 7: 153–160.

    CAS  Google Scholar 

  • Mattia M, Del Giudice P (2000) Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Computation 12: 2305–2329.

    Article  PubMed  CAS  Google Scholar 

  • Migliore M, Hoffman DA, Magee JC, Johnston D (1999) Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons. J. Comput. Neurosci 7: 5–16.

    Article  PubMed  CAS  Google Scholar 

  • Morrison A, Mehring C, Geisel T, Aertsen A, Diesmann A (2005) Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing, Neural Comp. 17: 1776–1801.

    Article  Google Scholar 

  • Myers R (2000) http://www.mtsu.edu/~csjudy/STL/HashMap.h

  • Santhakumar V, Aradi I, 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 

  • Wilson EC, Goodman PH, Harris FC (2001) Implementation of a Biologically Realistic Parallel Neocortical-Neural Network Simulator. Proceedings of the Tenth SIAM Conf. on Parallel Process. for Sci. Comp. March 12–14, 2001 Portsmouth, Virginia.

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Correspondence to M. Migliore.

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Migliore, M., Cannia, C., Lytton, W.W. et al. Parallel network simulations with NEURON. J Comput Neurosci 21, 119–129 (2006). https://doi.org/10.1007/s10827-006-7949-5

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  • DOI: https://doi.org/10.1007/s10827-006-7949-5

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