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@INPROCEEDINGS{Gutzen:864951,
      author       = {Gutzen, Robin and von Papen, Michael and Trensch, Guido and
                      Quaglio, Pietro and Grün, Sonja and Denker, Michael},
      title        = {{E}valuating neural network models within a formal
                      validation framework},
      reportid     = {FZJ-2019-04543},
      year         = {2019},
      abstract     = {To bridge the gap between the theory of neuronal networks
                      and findings obtained by the analysis of experimental data,
                      advances in computational neuroscience rely heavily on
                      simulations of neuronal network models. The verification of
                      model implementations and validation of its simulation
                      results is thus an indispensable part of any simulation
                      workflow. Moreover, in face of the heterogeneity of models
                      and simulators, approaches to enable the comparison between
                      model implementations is an issue of increasing importance
                      which calls for the establishment of a formalized validation
                      scheme. Although the bottom-up validation of cell response
                      properties is important, it does not automatically entail
                      the validity of the simulation dynamics on the network
                      scale. Here, we discuss a set of tests to assess the network
                      dynamics to attain a quantified level of agreement with a
                      given reference.We developed NetworkUnit $(RRID:SCR_016543;$
                      github.com/INM-6/NetworkUnit) as a Python library, built on
                      top of the SciUnit $(RRID:SCR_014528)$ framework [1,2], as
                      formal implementation of this validation process for
                      network-level validation testing, which complements
                      NeuronUnit $(RRID:SCR_015634)$ for the single cell level.
                      The toolbox Elephant $(RRID:SCR_003833)$ provides the
                      foundation to extract well-defined and comparable features
                      of the network dynamics. We demonstrate the use of the
                      library in a validation testing workflow [3,4] using a
                      worked example involving the SpiNNaker neuromorphic
                      system.References1 Omar, C., Aldrich, J., and Gerkin, R. C.
                      (2014). “Collaborative infrastructure for test-driven
                      scientific model validation”. Companion Proceedings of the
                      36th International Conference on Software Engineering - ICSE
                      Companion 2014, pages 524–527., 10.1145/2591062.25911292
                      Sarma, G. P., Jacobs, T. W., Watts, M. D., Ghayoomie, S. V.,
                      Larson, S. D., and Gerkin, R. C. (2016). “Unit testing,
                      model validation, and biological simulation”. F1000
                      Research, 5:1946., 10.12688/f1000research.9315.13 Gutzen,
                      R., von Papen M., Trensch G., Quaglio P., Grün S., and
                      Denker M. (2018). “Reproducible Neural Network
                      Simulations: Statistical Methods for Model Validation on the
                      Level of Network Activity Data”. Frontiers in
                      Neuroinformatics 12:90., 10.3389/fninf.2018.000904 Trensch,
                      G., Gutzen R., Blundell I., Denker M., and Morrison A.
                      (2018). “Rigorous Neural Network Simulations: A Model
                      Substantiation Methodology for Increasing the Correctness of
                      Simulation Results in the Absence of Experimental Validation
                      Data”. Frontiers in Neuroinformatics 12:81.,
                      10.3389/fninf.2018.00081},
      month         = {Sep},
      date          = {2019-09-01},
      organization  = {INCF Neuroinformatics, Warsaw
                       (Poland), 1 Sep 2019 - 2 Sep 2019},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
                      modelling and simulation (POF3-574) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/864951},
}