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@INPROCEEDINGS{Senk:886165,
      author       = {Senk, Johanna and Kriener, Birgit and Voges, Nicole and
                      Schüttler, Lisa and Gramelsberger, Gabriele and Plesser,
                      Hans Ekkehard and Diesmann, Markus and van Albada, Sacha},
      title        = {{S}ystematic textual and graphical description of
                      connectivity},
      reportid     = {FZJ-2020-04298},
      year         = {2020},
      abstract     = {Sustainable research on neuronal network models requires
                      published models to be understandable, reproducible, and
                      extendable. Left-out details about mathematical concepts and
                      assumptions, algorithmic implementations, or
                      parameterizations adversely affect progress. Such flaws are
                      unfortunately frequent and one reason is a lack of readily
                      applicable standards and tools for model description [1].
                      Here, we review models made available by the Computational
                      Neuroscience community in databases like ModelDB [2] and
                      Open Source Brain [3], and investigate the corresponding
                      connectivity structures and their descriptions in both
                      manuscript and code. Based on this review, we derive a set
                      of connectivity concepts in combination with guidelines for
                      a comprehensive, complete, and concise description of
                      network connectivity. In particular, we propose a unified
                      graphical notation for network diagrams to foster an
                      intuitive understanding of network properties (compare [4]).
                      This work also aims to guide the implementation of
                      connection routines in simulation software like NEST [5] and
                      neuromorphic hardware systems.References1. Nordlie E et al.
                      (2009) Towards Reproducible Descriptions of Neuronal Network
                      Models. PLoS Comput Biol. 5(8):e1000456,
                      10.1371/journal.pcbi.10004562. McDougal R A et al. (2017)
                      Twenty years of ModelDB and beyond: building essential
                      modeling tools for the future of neuroscience. J Comput
                      Neurosci. 42:1-10, 10.1007/s10827-016-0623-73. Gleeson P et
                      al. (2019) Open Source Brain: A Collaborative Resource for
                      Visualizing, Analyzing, Simulating and Developing
                      Standardized Models of Neurons and Circuits. Neuron.
                      103(3):395-411.e5, 10.1016/j.neuron.2019.05.0194. Le Novère
                      N et al. (2009) The Systems Biology Graphical Notation. Nat
                      Biotechnol. 27(8):735-41, 10.1038/nbt.15585. Gewaltig M-O
                      and Diesmann M (2007). NEST (NEural Simulation Tool).
                      Scholarpedia. 2(4):1430, 10.4249/scholarpedia.1430},
      month         = {Sep},
      date          = {2020-09-29},
      organization  = {Bernstein Conference 2020, online
                       (online), 29 Sep 2020 - 1 Oct 2020},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / PhD no
                      Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405) / Advanced Computing Architectures
                      $(aca_20190115)$ / HBP SGA1 - Human Brain Project Specific
                      Grant Agreement 1 (720270) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / DEEP-EST -
                      DEEP - Extreme Scale Technologies (754304) / DigiBrain - DL:
                      DigiBrain - From genes to brain function in health and
                      disease $(248828_20200305)$ / COBRA - COmputing BRAin
                      signals (COBRA): Biophysical computations of electrical and
                      magnetic brain signals $(250128_20200305)$ / SPP 2041
                      347572269 - Integration von Multiskalen-Konnektivität und
                      Gehirnarchitektur in einem supercomputergestützten Modell
                      der menschlichen Großhirnrinde (347572269) / Brain-Scale
                      Simulations $(jinb33_20191101)$ / GRK 2416 - GRK 2416:
                      MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)PHD-NO-GRANT-20170405 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 / G:(EU-Grant)754304
                      / $G:(Grant)248828_20200305$ / $G:(Grant)250128_20200305$ /
                      G:(GEPRIS)347572269 / $G:(DE-Juel1)jinb33_20191101$ /
                      G:(GEPRIS)368482240},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.12751/NNCN.BC2020.0263},
      url          = {https://juser.fz-juelich.de/record/886165},
}