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@ARTICLE{vanderVlag:907606,
      author       = {van der Vlag, Michiel and Woodman, Marmaduke and Fousek,
                      Jan and Diaz, Sandra and Pérez Martín, Aarón and Jirsa ,
                      Viktor and Morrison, Abigail},
      title        = {{R}ate{ML}: {A} {C}ode {G}eneration {T}ool for {B}rain
                      {N}etwork {M}odels},
      journal      = {Frontiers in network physiology},
      volume       = {2},
      issn         = {2674-0109},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2022-02105},
      pages        = {826345},
      year         = {2022},
      abstract     = {Whole brain network models are now an established tool in
                      scientific and clinical research, however their use in a
                      larger workflow still adds significant informatics
                      complexity. We propose a tool, RateML, that enables users to
                      generate such models from a succinct declarative
                      description, in which the mathematics of the model are
                      described without specifying how their simulation should be
                      implemented. RateML builds on NeuroML’s Low Entropy Model
                      Specification (LEMS), an XML based language for specifying
                      models of dynamical systems, allowing descriptions of neural
                      mass and discretized neural field models, as implemented by
                      the Virtual Brain (TVB) simulator: the end user describes
                      their model’s mathematics once and generates and runs code
                      for different languages, targeting both CPUs for fast single
                      simulations and GPUs for parallel ensemble simulations. High
                      performance parallel simulations are crucial for tuning many
                      parameters of a model to empirical data such as functional
                      magnetic resonance imaging (fMRI), with reasonable execution
                      times on small or modest hardware resources. Specifically,
                      while RateML can generate Python model code, it enables
                      generation of Compute Unified Device Architecture C++ code
                      for NVIDIA GPUs. When a CUDA implementation of a model is
                      generated, a tailored model driver class is produced,
                      enabling the user to tweak the driver by hand and perform
                      the parameter sweep. The model and driver can be executed on
                      any compute capable NVIDIA GPU with a high degree of
                      parallelization, either locally or in a compute cluster
                      environment. The results reported in this manuscript show
                      that with the CUDA code generated by RateML, it is possible
                      to explore thousands of parameter combinations with a single
                      Graphics Processing Unit for different models, substantially
                      reducing parameter exploration times and resource usage for
                      the brain network models, in turn accelerating the research
                      workflow itself. This provides a new tool to create
                      efficient and broader parameter fitting workflows, support
                      studies on larger cohorts, and derive more robust and
                      statistically relevant conclusions about brain dynamics.},
      cin          = {JSC / INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / HDS LEE - Helmholtz School
                      for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612) / HBP SGA2 - Human Brain Project Specific
                      Grant Agreement 2 (785907) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / 5234 - Emerging NC
                      Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)HDS-LEE-20190612 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36926112},
      UT           = {WOS:001203807400001},
      doi          = {10.3389/fnetp.2022.826345},
      url          = {https://juser.fz-juelich.de/record/907606},
}