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 121 articles , 1 standard article )

Showing results 1 to 20 of 121.
Sorted by year (citations)

1 2 3 ... 5 6 7 next

  1. 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 \itin silico: modelling their electrophysiological behaviour in health and disease (2016)
  2. Kublik, Richard A.; Chopp, David L.: A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures (2016)
  3. Briant, Linford J.B.; Paton, Julian F.R.; Pickering, Anthony E.; Champneys, Alan R.: Modelling the vascular response to sympathetic postganglionic nerve activity (2015)
  4. Laing, Carlo R.; Kevrekidis, Ioannis G.: Equation-free analysis of spike-timing-dependent plasticity (2015)
  5. Li, Songting; Zhou, Douglas; Cai, David: Analysis of the dendritic integration of excitatory and inhibitory inputs using cable models (2015)
  6. Schmidhuber, Jürgen: Deep learning in neural networks: an overview (2015) ioport
  7. 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)
  8. 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)
  9. Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
  10. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  11. Sarhad, Jonathan; Carlson, Robert; Anderson, Kurt E.: Population persistence in river networks (2014)
  12. Adams, Samantha V.; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F.: Adaptive training of cortical feature maps for a robot sensorimotor controller (2013) ioport
  13. Catsigeras, Eleonora; Guiraud, Pierre: Integrate and fire neural networks, piecewise contractive maps and limit cycles (2013)
  14. Raba, Ashley E.; Cordeiro, Jonathan M.; Antzelevitch, Charles; Beaumont, Jacques: Extending the conditions of application of an inversion of the Hodgkin-Huxley gating model (2013)
  15. Tumanova, Natalija; Čiegis, Raimondas; Meilūnas, Mečislavas: Numerical analysis of nonlinear model of excited carrier decay (2013)
  16. Van Drongelen, Wim: Modeling neural activity (2013)
  17. Wybo, Willem A.M.; Stiefel, Klaus M.; Torben-Nielsen, Benjamin: The Green’s function formalism as a bridge between single- and multi-compartmental modeling (2013)
  18. Bray, Laurence C.Jayet; Anumandla, Sridhar R.; Thibeault, Corey M.; Hoang, Roger V.; Goodman, Philip H.; Dascalu, Sergiu M.; Bryant, Bobby D.; Jr., Frederick C.Harris: Real-time human-robot interaction underlying neurorobotic trust and intent recognition (2012) ioport
  19. Čiegis, R.; Tumanova, N.: Stability analysis of implicit finite-difference schemes for parabolic problems on graphs (2012)
  20. Davies, S.; Galluppi, F.; Rast, A.D.; Furber, S.B.: A forecast-based STDP rule suitable for neuromorphic implementation (2012) ioport

1 2 3 ... 5 6 7 next