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@INPROCEEDINGS{Lu:1049767,
      author       = {Lu, Han and Fotiadou, Sinovia and Frings, Maren and Diaz,
                      Sandra and Hater, Thorsten and Zossimova, Ekaterina and van
                      der Vlag, Michiel},
      title        = {{A}ccelerating {B}rain {S}imulations using {H}igh
                      {P}erformance {C}omputing},
      reportid     = {FZJ-2025-05551},
      year         = {2025},
      abstract     = {High-performance computing (HPC) has become an
                      indispensable tool for scientific research, enabling the
                      simulation, analysis, and visualization of complex systems
                      across a wide range of disciplines. In neuroscience, HPC
                      empowers researchers to explore the intricate mechanisms of
                      brain functions, from single-cell dynamics to whole-brain
                      network interactions. Our work leverages HPC to model neural
                      dynamics across multiple scales, utilizing state-of-the-art
                      simulation tools. At the microscale, we employ the Arbor
                      simulator to investigate the dynamics of individual neurons,
                      capturing detailed biophysical processes. At the mesoscale,
                      we use the NEST simulator to model large-scale neural
                      networks with synaptic plasticity, shedding light on
                      mechanisms underlying learning and memory. At the
                      macroscale, we utilize The Virtual Brain (TVB) platform to
                      study whole-brain dynamics, providing insights into emergent
                      brain processes by integrating structural and functional
                      dynamics. The platform incorporates a GPU-accelerated
                      framework to alleviate the computational burden associated
                      with exploring large parameter spaces in brain simulations.
                      To further advance our understanding of brain function, we
                      have recently begun integrating these scales through
                      co-simulation approaches. For instance, we have combined
                      NEST and TVB to study interactions between large-scale
                      network activity and whole-brain dynamics. Similarly,
                      Arbor-TVB co-simulations enable us to bridge detailed
                      cellular processes with system-level behavior. These
                      multiscale models provide a more comprehensive understanding
                      of the brain’s fundamental mechanisms, offering new
                      perspectives on both healthy and pathological conditions
                      such as epilepsy and Alzheimer’s disease. A substantial
                      part of our work involves translating neuroscientific models
                      into code that can be efficiently distributed on HPC
                      systems. This requires a strong understanding of both the
                      underlying neuroscience and parallel programming. By
                      presenting our work at ISC 2025, we aim to advance the use
                      of HPC in neuroscience and bring researchers together to
                      tackle pressing challenges, such as memory constraints in
                      large-scale brain simulations.},
      month         = {Jun},
      date          = {2025-06-10},
      organization  = {ISC High Performance 2025, Hamburg
                       (Germany), 10 Jun 2025 - 13 Jun 2025},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1049767},
}