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001029459 1001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b0$$eCorresponding author$$ufzj
001029459 1112_ $$aWorkshop "Stochastic Models of the Brain"$$cTorino$$d2023-09-12 - 2023-09-13$$wItaly
001029459 245__ $$aLarge-scale network models as digital twins advance theory and neuromorphic computing$$f2023-09-12 - 
001029459 260__ $$c2023
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001029459 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1730981478_7985$$xInvited
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001029459 520__ $$aLarge-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
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