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@ARTICLE{Senk:907681,
      author       = {Senk, Johanna and Kriener, Birgit and Djurfeldt, Mikael and
                      Voges, Nicole and Jiang, Han-Jia and Schüttler, Lisa and
                      Gramelsberger, Gabriele and Diesmann, Markus and Plesser,
                      Hans E. and van Albada, Sacha J.},
      title        = {{C}onnectivity concepts in neuronal network modeling},
      journal      = {PLoS Computational Biology},
      volume       = {18},
      number       = {9},
      issn         = {1553-734X},
      reportid     = {FZJ-2022-02154},
      pages        = {e1010086},
      year         = {2022},
      abstract     = {Sustainable research on computational models of neuronal
                      networks requires published models to be understandable,
                      reproducible, and extendable. Missing details or ambiguities
                      about mathematical concepts and assumptions, algorithmic
                      implementations, or parameterizations hinder progress. Such
                      flaws are unfortunately frequent and one reason is a lack of
                      readily applicable standards and tools for model
                      description. Our work aims to advance complete and concise
                      descriptions of network connectivity but also to guide the
                      implementation of connection routines in simulation software
                      and neuromorphic hardware systems. We first review models
                      made available by the computational neuroscience community
                      in the repositories ModelDB and Open Source Brain, and
                      investigate the corresponding connectivity structures and
                      their descriptions in both manuscript and code. The review
                      comprises the connectivity of networks with diverse levels
                      of neuroanatomical detail and exposes how connectivity is
                      abstracted in existing description languages and simulator
                      interfaces. We find that a substantial proportion of the
                      published descriptions of connectivity is ambiguous. Based
                      on this review, we derive a set of connectivity concepts for
                      deterministically and probabilistically connected networks
                      and also address networks embedded in metric space. Beside
                      these mathematical and textual guidelines, we propose a
                      unified graphical notation for network diagrams to
                      facilitate an intuitive understanding of network properties.
                      Examples of representative network models demonstrate the
                      practical use of the ideas. We hope that the proposed
                      standardizations will contribute to unambiguous descriptions
                      and reproducible implementations of neuronal network
                      connectivity in computational neuroscience.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / 5235 -
                      Digitization of Neuroscience and User-Community Building
                      (POF4-523) / 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) / GRK 2416 - GRK
                      2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240) / SPP
                      2041 347572269 - Integration von Multiskalen-Konnektivität
                      und Gehirnarchitektur in einem supercomputergestützten
                      Modell der menschlichen Großhirnrinde (347572269) / ACA -
                      Advanced Computing Architectures (SO-092) / neuroIC001 -
                      NeuroModelingTalk (NMT) - Approaching the complexity barrier
                      in neuroscientific modeling (EXS-SF-neuroIC001) / MetaMoSim
                      - Generic metadata management for reproducible
                      high-performance-computing simulation workflows - MetaMoSim
                      (ZT-I-PF-3-026) / Open-Access-Publikationskosten
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-HGF)POF4-5235 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907 / G:(EU-Grant)945539
                      / G:(EU-Grant)754304 / G:(GEPRIS)368482240 /
                      G:(GEPRIS)347572269 / G:(DE-HGF)SO-092 /
                      G:(DE-82)EXS-SF-neuroIC001 / G:(DE-Juel-1)ZT-I-PF-3-026 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000933363400001},
      doi          = {10.1371/journal.pcbi.1010086},
      url          = {https://juser.fz-juelich.de/record/907681},
}