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@INPROCEEDINGS{Naveau:172419,
      author       = {Naveau, Mikael and Butz-Ostendorf, Markus},
      title        = {{S}imulating structural plasticity of large scale networks
                      in {NEST}},
      reportid     = {FZJ-2014-05897},
      year         = {2014},
      abstract     = {The 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.},
      month         = {Jul},
      date          = {2014-07-26},
      organization  = {Twenty Third Annual Computational
                       Neuroscience Meeting, Quebec City
                       (Canada), 26 Jul 2014 - 31 Jul 2014},
      subtyp        = {Other},
      cin          = {JSC / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {411 - Computational Science and Mathematical Methods
                      (POF2-411) / SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-411 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/172419},
}