Parallel network simulations with NEURON

  • M. MiglioreEmail author
  • C. Cannia
  • W. W. Lytton
  • Henry Markram
  • M. L. Hines


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.


Computer simulation Realistic modeling Parallel computation Spiking networks 


  1. 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.Google Scholar
  2. 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.PubMedGoogle Scholar
  3. Carriero N, Gelernter D (1989) Linda in context. Communications of the ACM, April 1989.Google Scholar
  4. Delorme A, Thorpe SJ (2003) SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons. Network 14: 613–627.PubMedCrossRefGoogle Scholar
  5. 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.PubMedCrossRefGoogle Scholar
  6. 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.Google Scholar
  7. Goddard N, Hood G, Howell F, Hines M, De Schutter E (2001) NEOSIM: Portable large-scale plug and play modelling. Neurocomputing 38–40: 1657–1661CrossRefGoogle Scholar
  8. 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
  9. Hines ML, Carnevale T (1997) The NEURON simulation environment. Neural Comp. 9: 178–1209.CrossRefGoogle Scholar
  10. Hines ML, Carnevale NT (2004) Discrete event simulation in the NEURON environment. Neurocomputing 58–60: 1117–1122.CrossRefGoogle Scholar
  11. Hindmarsh A, Serban R (2002) User documentation for CVODES, an ODE solver with sensitivity analysis capabilities.Tech. rep., Lawrence Livermore National Laboratory. CASC/sundials/.
  12. 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.CrossRefGoogle Scholar
  13. Karypis G, Kumar V (1998) Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Comput. 48(1): 96–129.CrossRefGoogle Scholar
  14. Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput. 17: 903–921.PubMedCrossRefGoogle Scholar
  15. Markram H (2006) The Blue Brain project, Nature Rev. Neurosci 7: 153–160.Google Scholar
  16. Mattia M, Del Giudice P (2000) Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Computation 12: 2305–2329.PubMedCrossRefGoogle Scholar
  17. 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.PubMedCrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.PubMedCrossRefGoogle Scholar
  20. 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.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • M. Migliore
    • 1
    • 2
    Email author
  • C. Cannia
    • 1
    • 3
  • W. W. Lytton
    • 4
  • Henry Markram
    • 5
  • M. L. Hines
    • 6
  1. 1.Institute of BiophysicsNational Research CouncilPalermoItaly
  2. 2.Department of NeurobiologyYale University School of MedicineNew HavenUSA
  3. 3.Dipartimento di Matematica e ApplicazioniUniversita' di PalermoItaly
  4. 4.Department of Physiology, Pharmacology and NeurologyState University of New YorkDownstate, BrooklynUSA
  5. 5.Laboratory of Neural MicrocircuitryBrain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL) 1015Switzerland
  6. 6.Department of Computer ScienceYale UniversityNew HavenUSA

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