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@ARTICLE{Trensch:858247,
      author       = {Trensch, Guido and Gutzen, Robin and Blundell, Inga and
                      Denker, Michael and Morrison, Abigail},
      title        = {{R}igorous {N}eural {N}etwork {S}imulations: {A} {M}odel
                      {S}ubstantiation {M}ethodology for {I}ncreasing the
                      {C}orrectness of {S}imulation {R}esults in the {A}bsence of
                      {E}xperimental {V}alidation {D}ata},
      journal      = {Frontiers in neuroinformatics},
      volume       = {12},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2018-07146},
      pages        = {81},
      year         = {2018},
      abstract     = {The reproduction and replication of scientific results is
                      an indispensable aspect of good scientific practice,
                      enabling previous studies to be built upon and increasing
                      our level of confidence in them. However, reproducibility
                      and replicability are not sufficient: an incorrect result
                      will be accurately reproduced if the same incorrect methods
                      are used. For the field of simulations of complex neural
                      networks, the causes of incorrect results vary from
                      insufficient model implementations and data analysis
                      methods, deficiencies in workmanship (e.g., simulation
                      planning, setup, and execution) to errors induced by
                      hardware constraints (e.g., limitations in numerical
                      precision). In order to build credibility, methods such as
                      verification and validation have been developed, but they
                      are not yet well-established in the field of neural network
                      modeling and simulation, partly due to ambiguity concerning
                      the terminology. In this manuscript, we propose a
                      terminology for model verification and validation in the
                      field of neural network modeling and simulation. We outline
                      a rigorous workflow derived from model verification and
                      validation methodologies for increasing model credibility
                      when it is not possible to validate against experimental
                      data. We compare a published minimal spiking network model
                      capable of exhibiting the development of polychronous
                      groups, to its reproduction on the SpiNNaker neuromorphic
                      system, where we consider the dynamics of several selected
                      network states. As a result, by following a formalized
                      process, we show that numerical accuracy is critically
                      important, and even small deviations in the dynamics of
                      individual neurons are expressed in the dynamics at network
                      level.},
      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          = {574 - Theory, modelling and simulation (POF3-574) / 511 -
                      Computational Science and Mathematical Methods (POF3-511) /
                      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)POF3-511 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30534066},
      UT           = {WOS:000451351200001},
      doi          = {10.3389/fninf.2018.00081},
      url          = {https://juser.fz-juelich.de/record/858247},
}