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@ARTICLE{Rinke:844224,
      author       = {Rinke, Sebastian and Butz-Ostendorf, Markus and Hermanns,
                      Marc-André and Naveau, Mikael and Wolf, Felix},
      title        = {{A} scalable algorithm for simulating the structural
                      plasticity of the brain},
      journal      = {Journal of parallel and distributed computing},
      volume       = {120},
      issn         = {0743-7315},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2018-01665},
      pages        = {251-266},
      year         = {2017},
      abstract     = {The 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.},
      cin          = {JARA-HPC / JSC},
      ddc          = {004},
      cid          = {$I:(DE-82)080012_20140620$ / I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / Scalable Performance
                      Analysis of Large-Scale Parallel Applications
                      $(jzam11_20091101)$ / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      $G:(DE-Juel1)jzam11_20091101$ / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000442193100021},
      doi          = {10.1016/j.jpdc.2017.11.019},
      url          = {https://juser.fz-juelich.de/record/844224},
}