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@INPROCEEDINGS{Diesmann:1044916,
      author       = {Diesmann, Markus},
      title        = {{B}rain models as digital twins advance theory and
                      neuromorphic {C}omputing},
      reportid     = {FZJ-2025-03438},
      year         = {2024},
      abstract     = {Computational neuroscience is entering a new era. This
                      originates from the convergence of two developments: First,
                      biological knowledge has expanded, enabling the construction
                      of anatomically detailed models of one or multiple brain
                      areas. Second, simulation has firmly established itself in
                      neuroscience as a third pillar alongside experiment and
                      theory. A conceptual separation has been achieved between
                      concrete network models and generic simulation engines.
                      Neuroscientists can now work with digital twins of certain
                      brain structures to test their ideas on brain functions and
                      probe the validity of approximations required for analytical
                      approaches.However, the use of this capability also requires
                      a change in mindset. Computational neuroscience seems stuck
                      at a certain level of model complexity for the last decade
                      not only because anatomical data were missing or because of
                      a lack of simulation technology. The fascination of the
                      field with minimal models leads to explanations for
                      individual mechanisms, but the reduction to the bare
                      equations required provides researchers with few contact
                      points to build on these works and construct larger systems
                      with a wider explanatory scope. In addition, creating
                      large-scale models goes beyond the period of an individual
                      PhD project. The change of perspective required is to view
                      digital twins as research platforms and scientific software
                      as infrastructure.As a concrete example, the presentation
                      discusses how the universality of mammalian brain structures
                      motivates the construction of large-scale models and
                      demonstrates how digital workflows help to reproduce results
                      and increase the confidence in such models. A digital twin
                      promotes neuroscientific investigations but can also serve
                      as a benchmark for technology. The talk shows how a model of
                      the cortical microcircuit has become a de facto standard for
                      neuromorphic computing [5] and has sparked a constructive
                      race in the community for ever larger computation speed and
                      lower energy consumption.},
      month         = {Oct},
      date          = {2024-10-15},
      organization  = {FIAS Neuroscience Seminar, Frankfurt
                       (Germany), 15 Oct 2024 - 16 Oct 2024},
      subtyp        = {Invited},
      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) / HiRSE -
                      Helmholtz Platform for Research Software Engineering
                      (HiRSE-20250220) / Brain-Scale Simulations
                      $(jinb33_20220812)$ / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-Juel-1)HiRSE-20250220 /
                      $G:(DE-Juel1)jinb33_20220812$ / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1044916},
}