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@ARTICLE{Vlachos:141627,
      author       = {Vlachos, Ioannis and Zaytsev, Yury and Spreizer, Sebastian
                      and Aertsen, Ad and Kumar, Arvind},
      title        = {{N}eural system prediction and identification challenge},
      journal      = {Frontiers in neuroinformatics},
      volume       = {7},
      number       = {43},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2013-06792},
      pages        = {1-10},
      year         = {2013},
      abstract     = {Can we infer the function of a biological neural network
                      (BNN) if we know the connectivity and activity of all its
                      constituent neurons? This question is at the core of
                      neuroscience and, accordingly, various methods have been
                      developed to record the activity and connectivity of as many
                      neurons as possible. Surprisingly, there is no theoretical
                      or computational demonstration that neuronal activity and
                      connectivity are indeed sufficient to infer the function of
                      a BNN. Therefore, we pose the Neural Systems Identification
                      and Prediction Challenge (nuSPIC). We provide the
                      connectivity and activity of all neurons and invite
                      participants (1) to infer the functions implemented
                      (hard-wired) in spiking neural networks (SNNs) by
                      stimulating and recording the activity of neurons and, (2)
                      to implement predefined mathematical/biological functions
                      using SNNs. The nuSPICs can be accessed via a web-interface
                      to the NEST simulator and the user is not required to know
                      any specific programming language. Furthermore, the nuSPICs
                      can be used as a teaching tool. Finally, nuSPICs use the
                      crowd-sourcing model to address scientific issues. With this
                      computational approach we aim to identify which functions
                      can be inferred by systematic recordings of neuronal
                      activity and connectivity. In addition, nuSPICs will help
                      the design and application of new experimental paradigms
                      based on the structure of the SNN and the presumed function
                      which is to be discovered.},
      cin          = {JSC / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {411 - Computational Science and Mathematical Methods
                      (POF2-411) / HASB - Helmholtz Alliance on Systems Biology
                      (HGF-SystemsBiology) / SMHB - Supercomputing and Modelling
                      for the Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-411 / G:(DE-Juel1)HGF-SystemsBiology /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000209207300040},
      pubmed       = {pmid:24399966},
      doi          = {10.3389/fninf.2013.00043},
      url          = {https://juser.fz-juelich.de/record/141627},
}