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@INPROCEEDINGS{vanderVlag:1019122,
      author       = {van der Vlag, Michiel and Diaz, Sandra},
      title        = {{V}ast {TVB} parameter space exploration: {A} {M}odular
                      {F}ramework for {A}ccelerating the {M}ulti-{S}cale
                      {S}imulation of {H}uman {B}rain {D}ynamics},
      reportid     = {FZJ-2023-05175},
      year         = {2022},
      abstract     = {Neural dynamics arise from the intricate multi-scale
                      structures of the brain, where neurons communicate through
                      synapses, forming transient assemblies that contribute to
                      global brain dynamics. Local network activity is regulated
                      by a complex interplay of intercellular communication,
                      intracellular signaling cascades, and the extracellular
                      molecular environment. Recent multi-scale models of brain
                      function have successfully linked the emergence of global
                      brain dynamics in both conscious and unconscious states to
                      microscopic changes influencing local networks.Specifically,
                      mean-field models, such as the Adaptive Exponential (AdEx)
                      models representing statistical properties of local neuron
                      populations, have been connected using human tractography
                      data to simulate multi-scale neural phenomena within The
                      Virtual Brain (TVB). While mean-field models can be run on
                      personal computers for short simulations or on
                      high-performance computing (HPC) architectures for longer
                      simulations, the computational demands remain high, leaving
                      extensive areas of the parameter space unexplored. In this
                      work, we introduce our TVB-HPC framework, a modular set of
                      methods designed to implement the TVB-AdEx model for GPU,
                      enhancing simulation speed and significantly reducing
                      computational resource requirements. This framework
                      maintains the stability and robustness of the TVB-AdEx
                      model, enabling more detailed exploration of vast parameter
                      spaces and longer simulations that were previously
                      challenging. Comparisons between our TVB-HPC framework and
                      TVB-AdEx demonstrate the similarity in generating patterns
                      of functional connectivity between brain regions. By varying
                      global coupling and spike-frequency adaptation, we reproduce
                      their interdependence in inducing transitions between
                      dynamics associated with conscious and unconscious brain
                      states. Exploring theparameter space further, we unveil a
                      nonlinear interplay between spike-frequency adaptation and
                      subthreshold adaptation, along with previously unnoticed
                      interactions between global coupling, adaptation, and the
                      propagation velocity of action potentials along the human
                      connectome. As our simulation and analysis toolkits are
                      openly accessible as open-source packages, our TVB-HPC
                      framework serves as a versatile template for scripting other
                      models. This approach facilitates the use of personalized
                      datasets in the study of inter-individual variability in
                      parameters related to functional brain dynamics.
                      Consequently, our results present potentially influential,
                      publicly-available methods for simulating and analyzing
                      various human brain states.},
      month         = {Dec},
      date          = {2023-12-05},
      organization  = {JSC's End-of-Year Colloquium 2023,
                       Jülich (Germany), 5 Dec 2023 - 5 Dec
                       2023},
      subtyp        = {Outreach},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HBP SGA3 - Human
                      Brain Project Specific Grant Agreement 3 (945539) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)945539 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1019122},
}