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@ARTICLE{Plesser:1046791,
      author       = {Plesser, Hans Ekkehard and Davison, Andrew P. and Diesmann,
                      Markus and Fukai, Tomoki and Gemmeke, Tobias and Gleeson,
                      Padraig and Knight, James C. and Nowotny, Thomas and René,
                      Alexandre and Rhodes, Oliver and Roque, Antonio C. and Senk,
                      Johanna and Schwalger, Tilo and Stadtmann, Tim and Tiddia,
                      Gianmarco and van Albada, Sacha},
      title        = {{B}uilding on {M}odels — {A} {P}erspective for
                      {C}omputational {N}euroscience},
      journal      = {Cerebral cortex},
      volume       = {35},
      number       = {11},
      issn         = {1047-3211},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2025-03958},
      pages        = {bhaf295},
      year         = {2025},
      abstract     = {Neural circuit models are essential for integrating
                      observations of the nervous system into a consistent whole.
                      Public sharing of well-documented codes for such models
                      facilitates further development. Nevertheless, scientific
                      practice in computational neuroscience suffers from
                      replication problems and little re-use of circuit models.
                      One exception is a data-driven model of early sensory cortex
                      by Potjans and Diesmann which has advanced computational
                      neuroscience as a building block for more complex models. As
                      a widely accepted benchmark for correctness and performance,
                      the model has driven the development of CPU-based,
                      GPU-based, and neuromorphic simulators. On the tenth
                      anniversary of the publication of this model, experts
                      convened at the Käte-Hamburger-Kolleg Cultures of Research
                      at RWTH Aachen University to reflect on the reasons for the
                      model’s success, its effect on computational neuroscience
                      and technology development, and the perspectives this offers
                      for the future of computational neuroscience. This report
                      summarizes the observations by the workshop participants.},
      cin          = {IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / 5234 -
                      Emerging NC Architectures (POF4-523) / BMBF 01UK2104 - Käte
                      Hamburger Kolleg "Kulturen des Forschens" (BMBF-01UK2104) /
                      JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027) / 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) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319) / MULTIRULES - Synaptic multi-factor learning
                      rules: from action potentials to behaviour (268689) / DFG
                      project G:(GEPRIS)313856816 - SPP 2041: Computational
                      Connectomics (313856816) / $HiRSE_PS$ - Helmholtz Platform
                      for Research Software Engineering - Preparatory Study
                      $(HiRSE_PS-20220812)$ / ACA - Advanced Computing
                      Architectures (SO-092) / BMBF 16ME0399 - Verbundprojekt:
                      Neuro-inspirierte Technologien der künstlichen Intelligenz
                      für die Elektronik der Zukunft - NEUROTEC II -
                      (BMBF-16ME0399) / BMBF 03ZU1106CA - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - A (03ZU1106CA) /
                      RenormalizedFlows - Transparent Deep Learning with
                      Renormalized Flows (BMBF-01IS19077A) / Brain-Scale
                      Simulations $(jinb33_20220812)$ / ICEI - Interactive
                      Computing E-Infrastructure for the Human Brain Project
                      (800858)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234 /
                      G:(DE-82)BMBF-01UK2104 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907 / G:(EU-Grant)945539
                      / G:(EU-Grant)101147319 / G:(EU-Grant)268689 /
                      G:(GEPRIS)313856816 / $G:(DE-Juel-1)HiRSE_PS-20220812$ /
                      G:(DE-HGF)SO-092 / G:(DE-82)BMBF-16ME0399 /
                      G:(BMBF)03ZU1106CA / G:(DE-Juel-1)BMBF-01IS19077A /
                      $G:(DE-Juel1)jinb33_20220812$ / G:(EU-Grant)800858},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1093/cercor/bhaf295},
      url          = {https://juser.fz-juelich.de/record/1046791},
}