NEURON

Parallel network simulations with NEURON. 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.


References in zbMATH (referenced in 144 articles , 1 standard article )

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  1. Dione, Ibrahima; Doyon, Nicolas; Deteix, Jean: Sensitivity analysis of the Poisson Nernst-Planck equations: a finite element approximation for the sensitive analysis of an electrodiffusion model (2019)
  2. Barranca, Victor J.; Zhu, Xiuqi George: A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes (2018)
  3. Jaramillo, Gabriela; Venkataramani, Shankar C.: Target patterns in a 2D array of oscillators with nonlocal coupling (2018)
  4. Kufel, Dominik S.; Wojcik, Grzegorz M.: Analytical modelling of temperature effects on an AMPA-type synapse (2018)
  5. Laing, Carlo R.: The dynamics of networks of identical theta neurons (2018)
  6. Morel, Danielle; Singh, Chandan; Levy, William B.: Linearization of excitatory synaptic integration at no extra cost (2018)
  7. Sadashivaiah, Vijay; Sacré, Pierre; Guan, Yun; Anderson, William S.; Sarma, Sridevi V.: Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter (2018)
  8. Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.: Hierarchical winner-take-all particle swarm optimization social network for neural model fitting (2017)
  9. Gandolfo, Daniel; Rodriguez, Roger; Tuckwell, Henry C.: Mean field analysis of large-scale interacting populations of stochastic conductance-based spiking neurons using the Klimontovich method (2017)
  10. Lin, Zhongwei; Tropper, Carl; McDougal, Robert A.; Ishlam Patoary, Mohammand Nazrul; Lytton, William W.; Yao, Yiping; Hines, Michael L.: Multithreaded stochastic PDES for reactions and diffusions in neurons (2017)
  11. Palmeri, Thomas J.; Love, Bradley C.; Turner, Brandon M.: Model-based cognitive neuroscience (2017)
  12. Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum: Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks (2017)
  13. Shepelev, I. A.; Shamshin, D. V.; Strelkova, G. I.; Vadivasova, T. E.: Bifurcations of spatiotemporal structures in a medium of FitzHugh-Nagumo neurons with diffusive coupling (2017)
  14. Ehling, Petra; Meuth, Patrick; Eichinger, Paul; Herrmann, Alexander M.; Bittner, Stefan; Pawlowski, Matthias; Pankratz, Susann; Herty, Michael; Budde, Thomas; Meuth, Sven G.: Human T cells \textitinsilico: modelling their electrophysiological behaviour in health and disease (2016)
  15. Kublik, Richard A.; Chopp, David L.: A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures (2016)
  16. Lytton, William W.; Seidenstein, Alexandra H.; Dura-Bernal, Salvador; McDougal, Robert A.; Schürmann, Felix; Hines, Michael L.: Simulation neurotechnologies for advancing brain research: parallelizing large networks in NEURON (2016)
  17. Appukuttan, Shailesh; Brain, Keith L.; Manchanda, Rohit: A computational model of urinary bladder smooth muscle syncytium. Validation and investigation of electrical properties (2015)
  18. Briant, Linford J. B.; Paton, Julian F. R.; Pickering, Anthony E.; Champneys, Alan R.: Modelling the vascular response to sympathetic postganglionic nerve activity (2015)
  19. Coskren, Patrick J.; Luebke, Jennifer I.; Kabaso, Doron; Wearne, Susan L.; Yadav, Aniruddha; Rumbell, Timothy; Hof, Patrick R.; Weaver, Christina M.: Functional consequences of age-related morphologic changes to pyramidal neurons of the rhesus monkey prefrontal cortex (2015)
  20. de Sousa, Giseli; Maex, Reinoud; Adams, Rod; Davey, Neil; Steuber, Volker: Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances (2015)

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