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@INPROCEEDINGS{Diesmann:1029469,
      author       = {Diesmann, Markus},
      title        = {{L}arge-scale network models as digital twins advance
                      theory and neuromorphic computing},
      reportid     = {FZJ-2024-05144},
      year         = {2024},
      abstract     = {Large-scale brain models as digital twins advance theory
                      and neuromorphic computingMarkus DiesmannComputational
                      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. The models
                      are formulated at the resolution of individual nerve cells
                      (neurons), represent the respective part of the brain with
                      its natural number of neurons, and are multi-scale. Next to
                      the spiking activity of neurons, also mesoscopic signals
                      like the local field potential (LFP) and fMRI signals can be
                      generated (e.g. [1]). Second, with the completion of the
                      European Human Brain Project (HBP), 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 [2,3]. Many different models can be
                      simulated with the same engine, such that these codes can
                      continuously be optimized [4] and operated as an
                      infrastructure. Network models with millions of neurons can
                      routinely be investigated (e.g. [4]). 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 efficient 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, but an exclusive focus
                      on hypothesis-driven research may prevent such sustained
                      constructive work. Possibly, researchers may also just be
                      missing the digital workflows to reuse large-scale models
                      and extend them reproducibly. 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 energy consumption of present AI systems is
                      unsustainable and undemocratic. Understanding the energy
                      efficiency of the brain may uncover pathways out of the
                      dilemma. 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.[1] Senk J, Hagen E, van Albada SJ, Diesmann M
                      (2018) Reconciliation of weak pairwise spike-train
                      correlations and highly coherent local field potentials
                      across space. arXiv:1805.10235 [q-bio.NC][2] Einevoll GT,
                      Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M,
                      Migliore M, Ness TV, Plesser HE, Schürmann F (2019) The
                      Scientific Case for Brain Simulations. Neuron 102:735-744[3]
                      Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ,
                      Schüttler L, Gramelsberger G, Diesmann M, Plesser HE, van
                      Albada SJ (2022) Connectivity concepts in neuronal network
                      modeling. PLOS Comput Biol 18(9):e1010086[4] Tiddia G,
                      Golosio B, Albers J, Senk J, Simula F, Pronold J, Fanti V,
                      Pastorelli E, Paolucci PS, van Albada SJ (2022) Fast
                      Simulation of a Multi-Area Spiking Network Model of Macaque
                      Cortex on an MPI-GPU Cluster. Front Neuroinform 16:883333
                      [5] Kurth AC, Senk J, Terhorst D, Finnerty J, Diesmann M
                      (2022) Sub-realtime simulation of a neuronal network of
                      natural density. Neuromorphic Computing and Engineering
                      2:021001keywords: simulation as third pillar, software as
                      infrastructure, universality of cortex, cellular-resolution
                      cortical microcircuit, multi-area model, neuromorphic
                      computing},
      month         = {Apr},
      date          = {2024-04-08},
      organization  = {Pucon Learning and AI Summit, Pucon
                       (Chile), 8 Apr 2024 - 12 Apr 2024},
      subtyp        = {Plenary/Keynote},
      cin          = {IAS-6 / INM-10},
      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) / 5235 - Digitization
                      of Neuroscience and User-Community Building (POF4-523) / ACA
                      - Advanced Computing Architectures (SO-092) / EBRAINS 2.0 -
                      EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319) / HBP - The Human
                      Brain Project (604102) / Helmholtz Platform for Research
                      Software Engineering - Preparatory Study
                      $(HiRSE_PS-20220812)$ / Brain-Scale Simulations
                      $(jinb33_20220812)$ / BMBF 03ZU1106CB - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - B
                      (BMBF-03ZU1106CB) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234 /
                      G:(DE-HGF)POF4-5235 / G:(DE-HGF)SO-092 /
                      G:(EU-Grant)101147319 / G:(EU-Grant)604102 /
                      $G:(DE-Juel-1)HiRSE_PS-20220812$ /
                      $G:(DE-Juel1)jinb33_20220812$ / G:(DE-Juel1)BMBF-03ZU1106CB
                      / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1029469},
}