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@ARTICLE{Nowke:848193,
      author       = {Nowke, Christian and Diaz, Sandra and Weyers, Benjamin and
                      Hentschel, Bernd and Morrison, Abigail and Kuhlen, Torsten
                      W. and Peyser, Alexander},
      title        = {{T}oward {R}igorous {P}arameterization of
                      {U}nderconstrained {N}eural {N}etwork {M}odels {T}hrough
                      {I}nteractive {V}isualization and {S}teering of
                      {C}onnectivity {G}eneration},
      journal      = {Frontiers in neuroinformatics},
      volume       = {12},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2018-03459},
      pages        = {32},
      year         = {2018},
      abstract     = {Simulation models in many scientific fields can have
                      non-unique solutions or unique solutions which can be
                      difficult to find. Moreover, in evolving systems, unique
                      final state solutions can be reached by multiple different
                      trajectories. Neuroscience is no exception. Often, neural
                      network models are subject to parameter fitting to obtain
                      desirable output comparable to experimental data. Parameter
                      fitting without sufficient constraints and a systematic
                      exploration of the possible solution space can lead to
                      conclusions valid only around local minima or around
                      non-minima. To address this issue, we have developed an
                      interactive tool for visualizing and steering parameters in
                      neural network simulation models. In this work, we focus
                      particularly on connectivity generation, since finding
                      suitable connectivity configurations for neural network
                      models constitutes a complex parameter search scenario. The
                      development of the tool has been guided by several use
                      cases—the tool allows researchers to steer the parameters
                      of the connectivity generation during the simulation, thus
                      quickly growing networks composed of multiple populations
                      with a targeted mean activity. The flexibility of the
                      software allows scientists to explore other connectivity and
                      neuron variables apart from the ones presented as use cases.
                      With this tool, we enable an interactive exploration of
                      parameter spaces and a better understanding of neural
                      network models and grapple with the crucial problem of
                      non-unique network solutions and trajectories. In addition,
                      we observe a reduction in turn around times for the
                      assessment of these models, due to interactive visualization
                      while the simulation is computed.},
      cin          = {JSC / INM-6 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406 /
                      $I:(DE-82)080012_20140620$},
      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) / Virtual
                      Connectomics - Deutschland - USA Zusammenarbeit in
                      Computational Science: Mechanistische Zusammenhänge
                      zwischen Struktur und funktioneller Dynamik im menschlichen
                      Gehirn (BMBF-01GQ1504B) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      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)BMBF-01GQ1504B / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29937723},
      UT           = {WOS:000433903200001},
      doi          = {10.3389/fninf.2018.00032},
      url          = {https://juser.fz-juelich.de/record/848193},
}