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@ARTICLE{Senk:1043040,
      author       = {Senk, Johanna and Kurth, Anno C. and Furber, Steve and
                      Gemmeke, Tobias and Golosio, Bruno and Heittmann, Arne and
                      Knight, James C. and Müller, Eric and Noll, Tobias and
                      Nowotny, Thomas and Coppola, Gorka Peraza and Peres, Luca
                      and Rhodes, Oliver and Rowley, Andrew and Schemmel, Johannes
                      and Stadtmann, Tim and Tetzlaff, Tom and Tiddia, Gianmarco
                      and van Albada, Sacha J. and Villamar, José and Diesmann,
                      Markus},
      title        = {{C}onstructive community race: full-density spiking neural
                      network model drives neuromorphic computing},
      journal      = {arXiv},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-02733},
      year         = {2025},
      abstract     = {The local circuitry of the mammalian brain is a focus of
                      the search for generic computational principles because it
                      is largely conserved across species and modalities. In 2014
                      a model was proposed representing all neurons and synapses
                      of the stereotypical cortical microcircuit below
                      $1\,\text{mm}^2$ of brain surface. The model reproduces
                      fundamental features of brain activity but its impact
                      remained limited because of its computational demands. For
                      theory and simulation, however, the model was a breakthrough
                      because it removes uncertainties of downscaling, and larger
                      models are less densely connected. This sparked a race in
                      the neuromorphic computing community and the model became a
                      de facto standard benchmark. Within a few years real-time
                      performance was reached and surpassed at significantly
                      reduced energy consumption. We review how the computational
                      challenge was tackled by different simulation technologies
                      and derive guidelines for the next generation of benchmarks
                      and other domains of science.},
      keywords     = {Performance (cs.PF) (Other) / Distributed, Parallel, and
                      Cluster Computing (cs.DC) (Other) / FOS: Computer and
                      information sciences (Other)},
      cin          = {IAS-6 / INM-10},
      cid          = {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) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) /
                      $HiRSE_PS$ - Helmholtz Platform for Research Software
                      Engineering - Preparatory Study $(HiRSE_PS-20220812)$ / ACA
                      - Advanced Computing Architectures (SO-092)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-HGF)POF4-5235 /
                      G:(EU-Grant)101147319 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      $G:(DE-Juel-1)HiRSE_PS-20220812$ / G:(DE-HGF)SO-092},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2505.21185},
      howpublished = {arXiv:2505.21185},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2505.21185;\%\%$},
      doi          = {10.48550/arXiv.2505.21185},
      url          = {https://juser.fz-juelich.de/record/1043040},
}