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@ARTICLE{Blundell:857165,
      author       = {Blundell, Inga and Brette, Romain and Cleland, Thomas A.
                      and Close, Thomas G. and Coca, Daniel and Davison, Andrew P.
                      and Diaz, Sandra and Fernandez Musoles, Carlos and Gleeson,
                      Padraig and Goodman, Dan F. M. and Hines, Michael and
                      Hopkins, Michael W. and Kumbhar, Pramod and Lester, David R.
                      and Marin, Bóris and Morrison, Abigail and Müller, Eric
                      and Nowotny, Thomas and Peyser, Alexander and Plotnikov,
                      Dimitri and Richmond, Paul and Rowley, Andrew and Rumpe,
                      Bernhard and Stimberg, Marcel and Stokes, Alan B. and
                      Tomkins, Adam and Trensch, Guido and Woodman, Marmaduke and
                      Eppler, Jochen Martin},
      title        = {{C}ode {G}eneration in {C}omputational {N}euroscience: {A}
                      {R}eview of {T}ools and {T}echniques},
      journal      = {Frontiers in neuroinformatics},
      volume       = {12},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2018-06402},
      pages        = {68},
      year         = {2018},
      abstract     = {Advances in experimental techniques and computational power
                      allowing researchers to gather anatomical and
                      electrophysiological data at unprecedented levels of detail
                      have fostered the development of increasingly complex models
                      in computational neuroscience. Large-scale, biophysically
                      detailed cell models pose a particular set of computational
                      challenges, and this has led to the development of a number
                      of domain-specific simulators. At the other level of detail,
                      the ever growing variety of point neuron models increases
                      the implementation barrier even for those based on the
                      relatively simple integrate-and-fire neuron model.
                      Independently of the model complexity, all modeling methods
                      crucially depend on an efficient and accurate transformation
                      of mathematical model descriptions into efficiently
                      executable code. Neuroscientists usually publish model
                      descriptions in terms of the mathematical equations
                      underlying them. However, actually simulating them requires
                      they be translated into code. This can cause problems
                      because errors may be introduced if this process is carried
                      out by hand, and code written by neuroscientists may not be
                      very computationally efficient. Furthermore, the translated
                      code might be generated for different hardware platforms,
                      operating system variants or even written in different
                      languages and thus cannot easily be combined or even
                      compared. Two main approaches to addressing this issues have
                      been followed. The first is to limit users to a fixed set of
                      optimized models, which limits flexibility. The second is to
                      allow model definitions in a high level interpreted
                      language, although this may limit performance. Recently, a
                      third approach has become increasingly popular: using code
                      generation to automatically translate high level
                      descriptions into efficient low level code to combine the
                      best of previous approaches. This approach also greatly
                      enriches efforts to standardize simulator-independent model
                      description languages. In the past few years, a number of
                      code generation pipelines have been developed in the
                      computational neuroscience community, which differ
                      considerably in aim, scope and functionality. This article
                      provides an overview of existing pipelines currently used
                      within the community and contrasts their capabilities and
                      the technologies and concepts behind them.},
      cin          = {JSC / INM-6 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406 /
                      $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 574 - Theory, modelling and simulation
                      (POF3-574) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain
                      Project Specific Grant Agreement 1 (720270) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      Virtual Connectomics - Deutschland - USA Zusammenarbeit in
                      Computational Science: Mechanistische Zusammenhänge
                      zwischen Struktur und funktioneller Dynamik im menschlichen
                      Gehirn (BMBF-01GQ1504B) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-574 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(DE-Juel1)BMBF-01GQ1504B /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000449250100001},
      pubmed       = {pmid:30455637},
      doi          = {10.3389/fninf.2018.00068},
      url          = {https://juser.fz-juelich.de/record/857165},
}