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@ARTICLE{Weidel:819528,
      author       = {Weidel, Philipp and Djurfeldt, Mikael and Morrison, Abigail
                      and Duarte, Renato},
      title        = {{C}losed {L}oop {I}nteractions between {S}piking {N}eural
                      {N}etwork and {R}obotic {S}imulators {B}ased on {MUSIC} and
                      {ROS}},
      journal      = {Frontiers in neuroinformatics},
      volume       = {10},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2016-05171},
      pages        = {31},
      year         = {2016},
      abstract     = {In order to properly assess the function and computational
                      properties of simulated neural systems, it is necessary to
                      account for the nature of the stimuli that drive the system.
                      However, providing stimuli that are rich and yet both
                      reproducible and amenable to experimental manipulations is
                      technically challenging, and even more so if a closed-loop
                      scenario is required. In this work, we present a novel
                      approach to solve this problem, connecting robotics and
                      neural network simulators. We implement a middleware
                      solution that bridges the Robotic Operating System (ROS) to
                      the Multi-Simulator Coordinator (MUSIC). This enables any
                      robotic and neural simulators that implement the
                      corresponding interfaces to be efficiently coupled, allowing
                      real-time performance for a wide range of configurations.
                      This work extends the toolset available for researchers in
                      both neurorobotics and computational neuroscience, and
                      creates the opportunity to perform closed-loop experiments
                      of arbitrary complexity to address questions in multiple
                      areas, including embodiment, agency, and reinforcement
                      learning.},
      cin          = {INM-6 / IAS-6 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      RL-BRD-J - Neural network mechanisms of reinforcement
                      learning (BMBF-01GQ1343) / W2Morrison - W2/W3 Professorinnen
                      Programm der Helmholtzgemeinschaft (B1175.01.12) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)BMBF-01GQ1343 /
                      G:(DE-HGF)B1175.01.12 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27536234},
      UT           = {WOS:000380668600001},
      doi          = {10.3389/fninf.2016.00031},
      url          = {https://juser.fz-juelich.de/record/819528},
}