000844224 001__ 844224
000844224 005__ 20210129232828.0
000844224 0247_ $$2doi$$a10.1016/j.jpdc.2017.11.019
000844224 0247_ $$2ISSN$$a0743-7315
000844224 0247_ $$2ISSN$$a1096-0848
000844224 0247_ $$2WOS$$aWOS:000442193100021
000844224 037__ $$aFZJ-2018-01665
000844224 041__ $$aEnglish
000844224 082__ $$a004
000844224 1001_ $$00000-0002-1171-9024$$aRinke, Sebastian$$b0$$eCorresponding author
000844224 245__ $$aA scalable algorithm for simulating the structural plasticity of the brain
000844224 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2017
000844224 3367_ $$2DRIVER$$aarticle
000844224 3367_ $$2DataCite$$aOutput Types/Journal article
000844224 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1570523534_4590
000844224 3367_ $$2BibTeX$$aARTICLE
000844224 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000844224 3367_ $$00$$2EndNote$$aJournal Article
000844224 520__ $$aThe neural network in the brain is not hard-wired. Even in the mature brain, new connections between neurons are formed and existing ones are deleted, which is called structural plasticity. The dynamics of the connectome is key to understanding how learning, memory, and healing after lesions such as stroke work. However, with current experimental techniques even the creation of an exact static connectivity map, which is required for various brain simulations, is very difficult. One alternative is to use network models to simulate the evolution of synapses between neurons based on their specified activity targets. This is particularly useful as experimental measurements of the spiking frequency of neurons are more easily accessible and reliable than biological connectivity data. The Model of Structural Plasticity (MSP) by Butz and van Ooyen is an example of this approach. However, to predict which neurons connect to each other, the current MSP model computes probabilities for all pairs of neurons, resulting in a complexity . To enable large-scale simulations with millions of neurons and beyond, this quadratic term is prohibitive. Inspired by hierarchical methods for solving -body problems in particle physics, we propose a scalable approximation algorithm for MSP that reduces the complexity to without any notable impact on the quality of the results. We show that an MPI-based parallel implementation of our scalable algorithm can simulate the structural plasticity of up to neurons—four orders of magnitude more than the naïve version.
000844224 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000844224 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x1
000844224 536__ $$0G:(DE-Juel1)jzam11_20091101$$aScalable Performance Analysis of Large-Scale Parallel Applications (jzam11_20091101)$$cjzam11_20091101$$fScalable Performance Analysis of Large-Scale Parallel Applications$$x2
000844224 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000844224 588__ $$aDataset connected to CrossRef
000844224 7001_ $$0P:(DE-HGF)0$$aButz-Ostendorf, Markus$$b1
000844224 7001_ $$0P:(DE-Juel1)168253$$aHermanns, Marc-André$$b2
000844224 7001_ $$0P:(DE-Juel1)157988$$aNaveau, Mikael$$b3
000844224 7001_ $$0P:(DE-HGF)0$$aWolf, Felix$$b4
000844224 773__ $$0PERI:(DE-600)1469781-6$$a10.1016/j.jpdc.2017.11.019$$gp. S0743731517303313$$p251-266$$tJournal of parallel and distributed computing$$v120$$x0743-7315$$y2017
000844224 8564_ $$uhttps://juser.fz-juelich.de/record/844224/files/1-s2.0-S0743731517303313-main.pdf$$yRestricted
000844224 8564_ $$uhttps://juser.fz-juelich.de/record/844224/files/1-s2.0-S0743731517303313-main.pdf?subformat=pdfa$$xpdfa$$yRestricted
000844224 909CO $$ooai:juser.fz-juelich.de:844224$$pVDB
000844224 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168253$$aForschungszentrum Jülich$$b2$$kFZJ
000844224 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)157988$$aForschungszentrum Jülich$$b3$$kFZJ
000844224 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000844224 9141_ $$y2018
000844224 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ PARALLEL DISTR COM : 2015
000844224 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000844224 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000844224 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000844224 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000844224 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000844224 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000844224 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000844224 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000844224 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000844224 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000844224 920__ $$lyes
000844224 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x0
000844224 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x1
000844224 980__ $$ajournal
000844224 980__ $$aVDB
000844224 980__ $$aI:(DE-82)080012_20140620
000844224 980__ $$aI:(DE-Juel1)JSC-20090406
000844224 980__ $$aUNRESTRICTED