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 78 articles , 1 standard article )

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  1. Kublik, Richard A.; Chopp, David L.: A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures (2016)
  2. Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
  3. Čiegis, R.; Tumanova, N.: Stability analysis of implicit finite-difference schemes for parabolic problems on graphs (2012)
  4. Davies, S.; Galluppi, F.; Rast, A.D.; Furber, S.B.: A forecast-based STDP rule suitable for neuromorphic implementation (2012)
  5. Kannon, Takayuki; Inagaki, Keiichiro; Kamiji, Nilton L.; Makimura, Kouji; Usui, Shiro: PLATO: data-oriented approach to collaborative large-scale brain system modeling (2011)
  6. Korogod, Sergey M.; Kaspirzhny, Anton V.: Spatial heterogeneity of passive electrical transfer properties of neuronal dendrites due to their metrical asymmetry (2011)
  7. Neftci, Emre; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney: A systematic method for configuring VLSI networks of spiking neurons (2011)
  8. Pospischil, Martin; Piwkowska, Zuzanna; Bal, Thierry; Destexhe, Alain: Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro (2011)
  9. Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve: Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware (2011)
  10. Yao, Wei; Huang, Huaxiong; Miura, Robert M.: A continuum neuronal model for the instigation and propagation of cortical spreading depression (2011)
  11. Chen, Jen-Yung: A simulation study investigating the impact of dendritic morphology and synaptic topology on neuronal firing patterns (2010)
  12. Čiegis, R.; Tumanova, N.: Finite-difference schemes for parabolic problems on graphs (2010)
  13. Kuznetsova, Anna Y.; Huertas, Marco A.; Kuznetsov, Alexey S.; Paladini, Carlos A.; Canavier, Carmen C.: Regulation of firing frequency in a computational model of a midbrain dopaminergic neuron (2010)
  14. Xylouris, Konstantinos; Queisser, Gillian; Wittum, Gabriel: A three-dimensional mathematical model of active signal processing in axons (2010)
  15. Destexhe, Alain: Self-sustained asynchronous irregular states and up-down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons (2009)
  16. Plesser, Hans E.; Diesmann, Markus: Simplicity and efficiency of integrate-and-fire neuron models (2009)
  17. Steephen, John Eric; Manchanda, Rohit: Differences in biophysical properties of nucleus accumbens medium spiny neurons emerging from inactivation of inward rectifying potassium currents (2009)
  18. Stiefel, Klaus M.; Gutkin, Boris S.; Sejnowski, Terrence J.: The effects of cholinergic neuromodulation on neuronal phase-response curves of modeled cortical neurons (2009)
  19. Takahashi, Masaaki; Natsume, Kiyohisa: Automatic estimation of the dynamics of channel conductance using a recurrent neural network (2009)
  20. Aisa, Brad; Mingus, Brian; O’reilly, Randy: The emergent neural modeling system (2008)

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