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001044916 005__ 20251024202103.0
001044916 037__ $$aFZJ-2025-03438
001044916 1001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b0$$eCorresponding author
001044916 1112_ $$aFIAS Neuroscience Seminar$$cFrankfurt$$d2024-10-15 - 2024-10-16$$wGermany
001044916 245__ $$aBrain models as digital twins advance theory and neuromorphic Computing$$f2024-10-16 - 
001044916 260__ $$c2024
001044916 3367_ $$033$$2EndNote$$aConference Paper
001044916 3367_ $$2DataCite$$aOther
001044916 3367_ $$2BibTeX$$aINPROCEEDINGS
001044916 3367_ $$2ORCID$$aLECTURE_SPEECH
001044916 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1761306804_21804$$xInvited
001044916 3367_ $$2DINI$$aOther
001044916 520__ $$aComputational 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.
001044916 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001044916 536__ $$0G:(DE-Juel-1)HiRSE-20250220$$aHiRSE - Helmholtz Platform for Research Software Engineering (HiRSE-20250220)$$cHiRSE-20250220$$x1
001044916 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x2
001044916 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x3
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001044916 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b0$$kFZJ
001044916 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001044916 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001044916 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
001044916 980__ $$atalk
001044916 980__ $$aVDB
001044916 980__ $$aI:(DE-Juel1)IAS-6-20130828
001044916 980__ $$aI:(DE-Juel1)INM-10-20170113
001044916 980__ $$aUNRESTRICTED