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@INPROCEEDINGS{Rinke:826032,
      author       = {Rinke, Sebastian and Butz-Ostendorf, Markus and Hermanns,
                      Marc-Andre and Naveau, Mikael and Wolf, Felix},
      title        = {{A} {S}calable {A}lgorithm for {S}imulating the
                      {S}tructural {P}lasticity of the {B}rain},
      publisher    = {IEEE},
      reportid     = {FZJ-2017-00301},
      pages        = {1-8},
      year         = {2016},
      note         = {ISBN 978-1-5090-6108-2},
      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
                      simulation based on network models to predict 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 et al. 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 O(n2). To enable
                      large-scale simulations with millions of neurons and beyond,
                      this quadratic term is prohibitive. Inspired by hierarchical
                      methods for solving n-body problems in particle physics, we
                      propose a scalable approximation algorithm for MSP that
                      reduces the complexity to O(n log2 n) without any notable
                      impact on the quality of the results. An MPI-based parallel
                      implementation of our scalable algorithm can simulate neuron
                      counts that exceed the state of the art by two orders of
                      magnitude.},
      date          = {10262016},
      organization  = {2016 28th International Symposium on
                       Computer Architecture and High
                       Performance Computing (SBAC-PAD), Los
                       Angeles (CA, USA), 26 Oct 2016 - 28 Oct
                       2016},
      cin          = {JSC / JARA-HPC},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000391392400001},
      doi          = {10.1109/SBAC-PAD.2016.9},
      url          = {https://juser.fz-juelich.de/record/826032},
}