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.
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References in zbMATH (referenced in 125 articles , 1 standard article )
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Sorted by year (- 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)
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- 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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- Briant, Linford J.B.; Paton, Julian F.R.; Pickering, Anthony E.; Champneys, Alan R.: Modelling the vascular response to sympathetic postganglionic nerve activity (2015)
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- 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)
- Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
- Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
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- Adams, Samantha V.; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F.: Adaptive training of cortical feature maps for a robot sensorimotor controller (2013) ioport
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- 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)
- Tumanova, Natalija; Čiegis, Raimondas; Meilūnas, Mečislavas: Numerical analysis of nonlinear model of excited carrier decay (2013)
- Van Drongelen, Wim: Modeling neural activity (2013)