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@ARTICLE{Herbers:903785,
      author       = {Herbers, Patrick and Calvo, Iago and Diaz, Sandra and
                      Robles Sanchez, Oscar David and Mata, Susana and Toharia,
                      Pablo and Pastor, Luis and Peyser, Alexander and Morrison,
                      Abigail and Klijn, Wouter},
      title        = {{C}on{G}en a simulator-agnostic visual language for
                      definition and generation of connectivity in large and
                      multiscale neural networks},
      journal      = {Frontiers in neuroinformatics},
      volume       = {15},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2021-05425},
      pages        = {766697},
      year         = {2022},
      abstract     = {An open challenge on the road to unraveling the brain's
                      multilevel organization is establishing techniques to
                      research connectivity and dynamics at different scales in
                      time and space, as well as the links between them. This work
                      focuses on the design of a framework that facilitates the
                      generation of multiscale connectivity in large neural
                      networks using a symbolic visual language capable of
                      representing the model at different structural
                      levels—ConGen. This symbolic language allows researchers
                      to create and visually analyze the generated networks
                      independently of the simulator to be used, since the visual
                      model is translated into a simulator-independent language.
                      The simplicity of the front end visual representation,
                      together with the simulator independence provided by the
                      back end translation, combine into a framework to enhance
                      collaboration among scientists with expertise at different
                      scales of abstraction and from different fields. On the
                      basis of two use cases, we introduce the features and
                      possibilities of our proposed visual language and associated
                      workflow. We demonstrate that ConGen enables the creation,
                      editing, and visualization of multiscale biological neural
                      networks and provides a whole workflow to produce simulation
                      scripts from the visual representation of the model.},
      cin          = {JSC / INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / HBP SGA2 - Human Brain Project Specific
                      Grant Agreement 2 (785907) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / 5234 - Emerging NC
                      Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35069166},
      UT           = {WOS:000758018300001},
      doi          = {10.3389/fninf.2021.766697},
      url          = {https://juser.fz-juelich.de/record/903785},
}