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@ARTICLE{Gutzen:858539,
      author       = {Gutzen, Robin and von Papen, Michael and Trensch, Guido and
                      Quaglio, Pietro and Grün, Sonja and Denker, Michael},
      title        = {{R}eproducible {N}eural {N}etwork {S}imulations:
                      {S}tatistical {M}ethods for {M}odel {V}alidation on the
                      {L}evel of {N}etwork {A}ctivity {D}ata},
      journal      = {Frontiers in neuroinformatics},
      volume       = {12},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2018-07410},
      pages        = {90},
      year         = {2018},
      abstract     = {Computational neuroscience relies on simulations of neural
                      network models to bridge the gap between the theory of
                      neural networks and the experimentally observed activity
                      dynamics in the brain. The rigorous validation of simulation
                      results against reference data is thus an indispensable part
                      of any simulation workflow. Moreover, the availability of
                      different simulation environments and levels of model
                      description require also validation of model implementations
                      against each other to evaluate their equivalence. Despite
                      rapid advances in the formalized description of models,
                      data, and analysis workflows, there is no accepted consensus
                      regarding the terminology and practical implementation of
                      validation workflows in the context of neural simulations.
                      This situation prevents the generic, unbiased comparison
                      between published models, which is a key element of
                      enhancing reproducibility of computational research in
                      neuroscience. In this study, we argue for the establishment
                      of standardized statistical test metrics that enable the
                      quantitative validation of network models on the level of
                      the population dynamics. Despite the importance of
                      validating the elementary components of a simulation, such
                      as single cell dynamics, building networks from validated
                      building blocks does not entail the validity of the
                      simulation on the network scale. Therefore, we introduce a
                      corresponding set of validation tests and present an example
                      workflow that practically demonstrates the iterative model
                      validation of a spiking neural network model against its
                      reproduction on the SpiNNaker neuromorphic hardware system.
                      We formally implement the workflow using a generic Python
                      library that we introduce for validation tests on neural
                      network activity data. Together with the companion study
                      (Trensch et al., sub.), the work presents a consistent
                      definition, formalization, and implementation of the
                      verification and validation process for neural network
                      simulations.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 571 -
                      Connectivity and Activity (POF3-571) / HBP SGA1 - Human
                      Brain Project Specific Grant Agreement 1 (720270) / HBP SGA2
                      - Human Brain Project Specific Grant Agreement 2 (785907) /
                      SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-571 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30618696},
      UT           = {WOS:000453919700001},
      doi          = {10.3389/fninf.2018.00090},
      url          = {https://juser.fz-juelich.de/record/858539},
}