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@INPROCEEDINGS{Hahne:825758,
      author       = {Hahne, Jan and Helias, Moritz and Kunkel, Susanne and
                      Igarashi, Jun and Kitayama, Itaru and Wylie, Brian and
                      Bolten, Matthias and Frommer, Andreas and Diesmann, Markus},
      title        = {{I}ncluding {G}ap {J}unctions into {D}istributed {N}euronal
                      {N}etwork {S}imulations},
      volume       = {10087},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2017-00070},
      isbn         = {978-3-319-50861-0 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {43 - 57},
      year         = {2016},
      comment      = {Brain-Inspired Computing / Amunts, Katrin (Editor) ; Cham :
                      Springer International Publishing, 2016, Chapter 4 ; ISSN:
                      0302-9743=1611-3349 ; ISBN:
                      978-3-319-50861-0=978-3-319-50862-7 ;
                      doi:10.1007/978-3-319-50862-7},
      booktitle     = {Brain-Inspired Computing / Amunts,
                       Katrin (Editor) ; Cham : Springer
                       International Publishing, 2016, Chapter
                       4 ; ISSN: 0302-9743=1611-3349 ; ISBN:
                       978-3-319-50861-0=978-3-319-50862-7 ;
                       doi:10.1007/978-3-319-50862-7},
      abstract     = {Contemporary simulation technology for neuronal networks
                      enables the simulation of brain-scale networks using neuron
                      models with a single or a few compartments. However,
                      distributed simulations at full cell density are still
                      lacking the electrical coupling between cells via so called
                      gap junctions. This is due to the absence of efficient
                      algorithms to simulate gap junctions on large parallel
                      computers. The difficulty is that gap junctions require an
                      instantaneous interaction between the coupled neurons,
                      whereas the efficiency of simulation codes for spiking
                      neurons relies on delayed communication. In a recent paper
                      [15] we describe a technology to overcome this obstacle.
                      Here, we give an overview of the challenges to include gap
                      junctions into a distributed simulation scheme for neuronal
                      networks and present an implementation of the new technology
                      available in the NEural Simulation Tool (NEST 2.10.0).
                      Subsequently we introduce the usage of gap junctions in
                      model scripts as well as benchmarks assessing the
                      performance and overhead of the technology on the
                      supercomputers JUQUEEN and K computer.},
      month         = {Jul},
      date          = {2015-07-06},
      organization  = {International Workshop on
                       Brain-Inspired Computing, Cetraro
                       (Italy), 6 Jul 2015 - 10 Jul 2015},
      cin          = {INM-6 / JSC / JARA-BRAIN},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)JSC-20090406 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 574 - Theory, modelling and simulation
                      (POF3-574) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain
                      Project Specific Grant Agreement 1 (720270) / Brain-Scale
                      Simulations $(jinb33_20121101)$ / Scalable solvers for
                      linear systems and time-dependent problems
                      $(hwu12_20141101)$ / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS) / ATMLPP - ATML Parallel Performance
                      (ATMLPP)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-574 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)720270 /
                      $G:(DE-Juel1)jinb33_20121101$ / $G:(DE-Juel1)hwu12_20141101$
                      / G:(DE-Juel1)Helmholtz-SLNS / G:(DE-Juel-1)ATMLPP},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-319-50862-7_4},
      url          = {https://juser.fz-juelich.de/record/825758},
}