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@INPROCEEDINGS{Senk:917354,
      author       = {Senk, Johanna},
      title        = {{O}n concepts, correctness, and performance of network
                      simulations},
      reportid     = {FZJ-2023-00582},
      year         = {2022},
      note         = {Session: Tools and formats for large scale network
                      modelling},
      abstract     = {The development of large-scale neuronal network models is
                      an iterative process and best described by a loop linking
                      the conceptual description with its algorithmic
                      implementation. Models are informed by experimental data and
                      insights from theory; their simulated dynamics needs to be
                      validated against experimentally observed activity. The
                      inherent interdisciplinarity and intricacy of this endeavor
                      make it prone to pitfalls that complicate the
                      comprehensibility and reproducibility of each step.
                      Addressing shortcomings in model descriptions, we review how
                      connectivity is specified in published models and propose
                      unified connectivity concepts and guidelines including a
                      graphical notation for network diagrams. Furthermore, we
                      focus on the challenge to compare simulations of a cortical
                      microcircuit model with respect to correctness and
                      performance across different simulation technologies;
                      literature shows a race for the fastest and most energy
                      efficient simulation. We also showcase a conceptual workflow
                      for performance benchmarking of the simulator NEST together
                      with the benchmarking framework beNNch as a reference
                      implementation. Finally, we introduce NNMT, a toolbox for
                      mean-field based analysis methods of neuronal network
                      models. The presented approaches aim at raising awareness to
                      common difficulties and fostering a sustainable and
                      collaborative modeling culture.},
      month         = {Sep},
      date          = {2022-09-13},
      organization  = {INCF Neuroinformatics Assembly 2022,
                       virtual (virtual), 13 Sep 2022 - 13 Sep
                       2022},
      subtyp        = {Invited},
      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          = {5231 - Neuroscientific Foundations (POF4-523) / 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 2041 - GRK 2041:
                      Modell Romantik. Variation - Reichweite - Aktualität
                      (250805958) / DFG project 347572269 - Heterogenität von
                      Zytoarchitektur, Chemoarchitektur und Konnektivität in
                      einem großskaligen Computermodell 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) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / MSNN -
                      Theory of multi-scale neuronal networks
                      (HGF-SMHB-2014-2018)},
      pid          = {G:(DE-HGF)POF4-5231 / 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)250805958 / G:(GEPRIS)347572269 /
                      G:(DE-HGF)SO-092 / G:(DE-82)EXS-SF-neuroIC001 /
                      G:(DE-Juel-1)ZT-I-PF-3-026 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/917354},
}