000172419 001__ 172419
000172419 005__ 20210129214431.0
000172419 0247_ $$2Handle$$a2128/8095
000172419 037__ $$aFZJ-2014-05897
000172419 041__ $$aEnglish
000172419 082__ $$a610
000172419 1001_ $$0P:(DE-Juel1)157988$$aNaveau, Mikael$$b0$$eCorresponding Author$$ufzj
000172419 1112_ $$aTwenty Third Annual Computational Neuroscience Meeting$$cQuebec City$$d2014-07-26 - 2014-07-31$$gCNS*2014$$wCanada
000172419 245__ $$aSimulating structural plasticity of large scale networks in NEST
000172419 260__ $$c2014
000172419 3367_ $$033$$2EndNote$$aConference Paper
000172419 3367_ $$2BibTeX$$aINPROCEEDINGS
000172419 3367_ $$2DRIVER$$aconferenceObject
000172419 3367_ $$2ORCID$$aCONFERENCE_POSTER
000172419 3367_ $$2DataCite$$aOutput Types/Conference Poster
000172419 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1570522472_2022$$xOther
000172419 520__ $$aThe brain is much less hard-wired as traditionally thought. Permanently, new synapses are formed, existing synapses are deleted or connectivity rewires by re-routing axonal branches (structural plasticity). However, all current large-scale neuronal network models are hard-wired with plasticity merely arising from changes in the strength of existing synapses, therefore missing an important aspect of the plasticity of brain networks. This project is to develop the first large-scale neuronal network model with structural plasticity in the neuronal network simulator NEST [1] and to make it scalable for HPC.Formation and deletion of synapses in the model for structural plasticity (MSP) [2] depends on the number of synaptic contact possibilities that each neuron has, i.e. the number of axonal boutons and dendritic spines. Therefore, we developed a framework that allows the addition of synaptic elements (i.e. axonal boutons or dendritic spines) for every neuron model already implemented in NEST. The user can then define its own synaptic elements and their corresponding growth dynamic depending on the electrical activity (see Figure 1). Synapses are formed by merging corresponding synaptic elements or are deleted when synaptic elements are lost. The update in connectivity depends on the availability of the synaptic elements in the entire networks. To make this model scalable for HPC, we developed a probabilistic approach that reduce both communication between compute nodes and their memory usage.This implementation of the MSP in NEST allows neuroscientists to address important scientific questions on how large-scale networks rewire their connectivity in response to distortions in electrical activity balances.1 - Gewaltig MO, Diesmann M. NEST (NEural Simulation Tool) Scholarpedia. 2007;15(4):1440.2 - Butz M, van Ooyen A. A Simple Rule for Dendritic Spine and Axonal Bouton Formation Can Account for Cortical Reorganization after Focal Retinal Lesions. PLoS Comput Biol 9. 2013;15:e1003259. doi: 10.1371/journal.pcbi.1003259.
000172419 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x0
000172419 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x1
000172419 588__ $$aDataset connected to CrossRef, juser.fz-juelich.de
000172419 65027 $$0V:(DE-MLZ)SciArea-160$$2V:(DE-HGF)$$aBiology$$x0
000172419 7001_ $$0P:(DE-Juel1)158019$$aButz-Ostendorf, Markus$$b1$$ufzj
000172419 8564_ $$uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4126386/
000172419 8564_ $$uhttps://juser.fz-juelich.de/record/172419/files/FZJ-2014-05897.pdf$$yOpenAccess
000172419 8564_ $$uhttps://juser.fz-juelich.de/record/172419/files/FZJ-2014-05897.jpg?subformat=icon-144$$xicon-144$$yOpenAccess
000172419 8564_ $$uhttps://juser.fz-juelich.de/record/172419/files/FZJ-2014-05897.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
000172419 8564_ $$uhttps://juser.fz-juelich.de/record/172419/files/FZJ-2014-05897.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
000172419 909CO $$ooai:juser.fz-juelich.de:172419$$pdriver$$pVDB$$popen_access$$popenaire
000172419 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)157988$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000172419 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)158019$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000172419 9132_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bPOF III$$lKey Technologies$$vSupercomputing & Big Data$$x0
000172419 9131_ $$0G:(DE-HGF)POF2-411$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vComputational Science and Mathematical Methods$$x0
000172419 9141_ $$y2014
000172419 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000172419 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000172419 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF <  5
000172419 920__ $$lyes
000172419 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000172419 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x1
000172419 980__ $$aposter
000172419 980__ $$aVDB
000172419 980__ $$aI:(DE-Juel1)JSC-20090406
000172419 980__ $$aI:(DE-82)080012_20140620
000172419 980__ $$aUNRESTRICTED
000172419 9801_ $$aFullTexts