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 131 articles , 1 standard article )

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  1. Jaramillo, Gabriela; Venkataramani, Shankar C.: Target patterns in a 2D array of oscillators with nonlocal coupling (2018)
  2. Laing, Carlo R.: The dynamics of networks of identical theta neurons (2018)
  3. 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)
  4. 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)
  5. 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)
  6. Palmeri, Thomas J.; Love, Bradley C.; Turner, Brandon M.: Model-based cognitive neuroscience (2017)
  7. 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)
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  9. Kublik, Richard A.; Chopp, David L.: A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures (2016)
  10. Briant, Linford J. B.; Paton, Julian F. R.; Pickering, Anthony E.; Champneys, Alan R.: Modelling the vascular response to sympathetic postganglionic nerve activity (2015)
  11. Laing, Carlo R.; Kevrekidis, Ioannis G.: Equation-free analysis of spike-timing-dependent plasticity (2015)
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  14. Yi, Guosheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xile; Deng, Bin; Han, Chunxiao: Spike-frequency adaptation of a two-compartment neuron modulated by extracellular electric fields (2015)
  15. Yu, Na; Canavier, Carmen C.: A mathematical model of a midbrain dopamine neuron identifies two slow variables likely responsible for bursts evoked by SK channel antagonists and terminated by depolarization block (2015)
  16. Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
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  18. Sarhad, Jonathan; Carlson, Robert; Anderson, Kurt E.: Population persistence in river networks (2014)
  19. Adams, Samantha V.; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F.: Adaptive training of cortical feature maps for a robot sensorimotor controller (2013) ioport
  20. Catsigeras, Eleonora; Guiraud, Pierre: Integrate and fire neural networks, piecewise contractive maps and limit cycles (2013)

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