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 187 articles , 1 standard article )

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  1. Barranca, Victor J.; Hu, Yolanda; Porterfield, Zoe; Rothstein, Samuel; Xuan, Alex: Data-driven reconstruction and encoding of sparse stimuli across convergent sensory layers from downstream neuronal network dynamics (2021)
  2. Breit, Markus; Queisser, Gillian: The necessary modeling detail for neuronal signaling: Poisson-Nernst-Planck and cable equation models in one and three dimensions (2021)
  3. Jones, Ilenna Simone; Kording, Konrad Paul: Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? (2021)
  4. Park, R.: A comparison of six numerical methods for integrating a compartmental Hodgkin-Huxley type model (2021)
  5. Bazenkov, Nikolay I.; Boldyshev, Boris A.; Dyakonova, Varvara; Kuznetsov, Oleg P.: Simulating small neural circuits with a discrete computational model (2020)
  6. Gilson, Matthieu; Pfister, Jean-Pascal: Propagation of spiking moments in linear Hawkes networks (2020)
  7. Tran, Harry; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie: Fast simulation of extracellular action potential signatures based on a morphological filtering approximation (2020)
  8. Andalibi, Vafa; Hokkanen, Henri; Vanni, Simo: Controlling complexity of cerebral cortex simulations. I: CxSystem, a flexible cortical simulation framework (2019)
  9. Chartrand, Thomas; Goldman, Mark S.; Lewis, Timothy J.: Synchronization of electrically coupled resonate-and-fire neurons (2019)
  10. Dai, Wei P.; Li, Songting; Zhou, Douglas: Fast algorithms for simulation of neuronal dynamics based on the bilinear dendritic integration rule (2019)
  11. 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)
  12. Farhoodi, Roozbeh; Filom, Khashayar; Jones, Ilenna Simone; Kording, Konrad Paul: On functions computed on trees (2019)
  13. Gupta, Suranjana; Manchanda, Rohit: A computational model of large conductance voltage and calcium activated potassium channels: implications for calcium dynamics and electrophysiology in detrusor smooth muscle cells (2019)
  14. Hajizadeh, Aida; Matysiak, Artur; May, Patrick J. C.; König, Reinhard: Explaining event-related fields by a mechanistic model encapsulating the anatomical structure of auditory cortex (2019)
  15. Hokkanen, Henri; Andalibi, Vafa; Vanni, Simo: Controlling complexity of cerebral cortex simulations. II: Streamlined microcircuits (2019)
  16. Ioan, Daniel; Bărbulescu, Ruxandra; Silveira, Luis Miguel; Ciuprina, Gabriela: Reduced order models of myelinated axonal compartments (2019)
  17. Malerba, Paola; Rulkov, Nikolai F.; Bazhenov, Maxim: Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3 (2019)
  18. Zhukov, Oleg A.; Kazakova, Tatiana A.; Maksimov, Georgy V.; Brazhe, Alexey R.: Cost of auditory sharpness: model-based estimate of energy use by auditory brainstem “octopus” neurons (2019)
  19. Zupanc, Günther K. H.; Amaro, Stephanie M.; Lehotzky, Dávid; Zupanc, Frederick B.; Leung, Nicholas Y.: Glia-mediated modulation of extracellular potassium concentration determines the sexually dimorphic output frequency of a model brainstem oscillator (2019)
  20. 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)

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