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dc.contributor.authorLytton, William W
dc.contributor.authorSeidenstein, Alexandra H
dc.contributor.authorDura-Bernal, Salvador
dc.contributor.authorMcDougal, Robert A
dc.contributor.authorSchürmann, Felix
dc.contributor.authorHines, Michael L
dc.date.accessioned2023-04-10T17:00:42Z
dc.date.available2023-04-10T17:00:42Z
dc.date.issued2016-08-24
dc.identifier.citationLytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schürmann F, Hines ML. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput. 2016 Oct;28(10):2063-90. doi: 10.1162/NECO_a_00876. Epub 2016 Aug 24. PMID: 27557104; PMCID: PMC5295685.en_US
dc.identifier.eissn1530-888X
dc.identifier.doi10.1162/NECO_a_00876
dc.identifier.pmid27557104
dc.identifier.urihttp://hdl.handle.net/20.500.12648/8575
dc.description.abstractLarge multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.
dc.language.isoenen_US
dc.relation.urlhttps://direct.mit.edu/neco/article-abstract/28/10/2063/8200/Simulation-Neurotechnologies-for-Advancing-Brainen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleSimulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleNeural computationen_US
dc.source.volume28
dc.source.issue10
dc.source.beginpage2063
dc.source.endpage90
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.description.versionAMen_US
refterms.dateFOA2023-04-10T17:00:43Z
html.description.abstractLarge multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.
dc.description.institutionSUNY Downstateen_US
dc.description.departmentPhysiology and Pharmacologyen_US
dc.description.departmentNathan Kline Institute for Psychiatric Researchen_US
dc.description.degreelevelN/Aen_US
dc.identifier.journalNeural computation


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