% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Diesmann:1029461,
      author       = {Diesmann, Markus},
      title        = {{L}arge-scale network models as digital twins advance
                      theory and neuromorphic computing},
      reportid     = {FZJ-2024-05136},
      year         = {2023},
      abstract     = {Simulation of large-scale neural networksIn this talk I
                      will criticize the present state of the field of
                      computational neuroscience by pointing out that minimal
                      models explain individual mechanisms, but do not confront
                      these ideas with the anatomical and physiological
                      constraints of the brain. 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. Furthermore, although the
                      brain is large, it has a finite number of elements, such
                      that downscaling the network size or taking limits to
                      infinity come with the risk of perturbing the correlation
                      structure. Why are larger models not routinely being built,
                      and why does computational neuroscience seem to be stuck at
                      a certain level of model complexity? There are multiple
                      reasons for this situation. On the conceptual level there is
                      a tradition in neuroscience to fund only hypothesis-driven
                      research; instrument-driven or platform-based research is
                      not common, although Nobel prizes are given for methods.
                      Additionally, constructing large-scale models goes beyond
                      the period of an individual PhD project. Possibly, doctoral
                      researchers may just be missing the digital tools to reuse
                      large-scale models and extend them reproducibly.
                      Nevertheless, simulation has firmly established itself in
                      neuroscience as a third pillar alongside experiment and
                      theory. With simulations, researchers can probe ideas beyond
                      what is presently treatable by analytical approaches or
                      accessible by experimental techniques. In addition,
                      simulation software has matured to a point where researchers
                      can collaborate by exchanging high-level model descriptions.
                      Digital workflows are becoming available which enable
                      researchers to collaborate on large-scale models as data
                      integration platforms. I argue that the change of
                      perspective required is to view software as scientific
                      infrastructure with all consequences for the requirements on
                      quality, long-term availability, and support.As a concrete
                      example, I discuss how the universality of mammalian cortex
                      has acted as a motivation to construct large-scale models,
                      and demonstrate how digital tools have helped to reproduce
                      results and increase the confidence in such models. The
                      accessibility of benchmark models drives further modeling
                      work, but also influences technological developments in the
                      field of neuromorphic computing.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. DOI:
                      10.1016/j.neuron.2019.03.027Aimone 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.2023.1157418keywords:
                      simulation as third pillar, software as infrastructure,
                      universality of cortex, cortical microcircuit, multi-area
                      model, neuromorphic computing},
      month         = {Oct},
      date          = {2023-10-05},
      organization  = {Workshop "MPI and NEST GPU", Cagliari
                       (Italy), 5 Oct 2023 - 6 Oct 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) /
                      BMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design
                      (Projekt C) - B (BMBF-03ZU1106CB) / ACA - Advanced Computing
                      Architectures (SO-092) / HBP - The Human Brain Project
                      (604102) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)945539 /
                      G:(DE-Juel1)BMBF-03ZU1106CB / G:(DE-HGF)SO-092 /
                      G:(EU-Grant)604102 / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1029461},
}