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@INPROCEEDINGS{Diesmann:1029459,
      author       = {Diesmann, Markus},
      title        = {{L}arge-scale network models as digital twins advance
                      theory and neuromorphic computing},
      reportid     = {FZJ-2024-05134},
      year         = {2023},
      abstract     = {Large-scale network 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, knowledge has been
                      accumulated enabling the construction of anatomically
                      detailed models of one or multiple brain areas. The models
                      have cellular and synaptic resolution, represent the
                      respective part of the brain with its natural number of
                      neurons and synapses, and are multi-scale. Next to spiking
                      activity, 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 simulation
                      codes can continuously be optimized and operated as an
                      infrastructure [4]. Network models with millions of neurons
                      can routinely be investigated. 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 new 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, constructing 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 with all consequences for the requirements on
                      quality, long-term availability, and support.As a concrete
                      example, the presentation discusses how the universality of
                      mammalian cortex has acted as a motivation to construct
                      large-scale models and demonstrates how digital workflows
                      have helped 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].[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] Aimone
                      JB, Awile O, Diesmann M, Knight JC, Nowotny T, Schürmann F
                      (2023) Editorial: Neuroscience, Computing, Performance, and
                      Benchmarks: Why It Matters to Neuroscience How Fast We Can
                      Compute. Front Neuroinform 17. DOI:
                      10.3389/fninf.2023.1157418[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         = {Sep},
      date          = {2023-09-12},
      organization  = {Workshop "Stochastic Models of the
                       Brain", Torino (Italy), 12 Sep 2023 -
                       13 Sep 2023},
      subtyp        = {Invited},
      cin          = {IAS-6 / INM-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      ACA - Advanced Computing Architectures (SO-092) / BMBF
                      03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt
                      C) - B (BMBF-03ZU1106CB) / EBRAINS 2.0 - EBRAINS 2.0: A
                      Research Infrastructure to Advance Neuroscience and Brain
                      Health (101147319) / HBP - The Human Brain Project (604102)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)945539 / G:(DE-HGF)SO-092
                      / G:(DE-Juel1)BMBF-03ZU1106CB / G:(EU-Grant)101147319 /
                      G:(EU-Grant)604102},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1029459},
}