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@INPROCEEDINGS{Linssen:865742,
      author       = {Linssen, Charl and Eppler, Jochen Martin and Morrison,
                      Abigail},
      title        = {{NESTML}: {A}n extensible modeling language for
                      biologically plausible neural networks},
      reportid     = {FZJ-2019-05059},
      year         = {2019},
      abstract     = {<p>NESTML [1, 2] was developed to address the
                      maintainability issues that follow from an increasing number
                      of models, model variants, and an increased model complexity
                      in computational neuroscience. Our aim is to ease the
                      modelling process for neuroscientists both with and without
                      prior training in computer science. This is achieved without
                      compromising on performance by automatic source-code
                      generation, allowing the same model file to target different
                      hardware or software platforms by changing a single
                      command-line parameter. While originally developed in the
                      context of the NEST Simulator [3], the language itself as
                      well as the associated toolchain are lightweight, modular
                      and extensible, by virtue of using a parser generator and
                      internal abstract syntax tree (AST) representation, which
                      can be operated on using well-known patterns such as
                      visitors and rewriting.</p><p>A typical workflow consists of
                      the following steps: Initially, a model of interest is
                      identified. This model might describe the dynamical
                      behaviour of a single neuron, or the plasticity rules
                      concerning a synapse. The model description is typically in
                      mathematical or textual form, and needs to be converted by
                      the neuroscientist into a format following the NESTML
                      syntax. It is then processed by invoking the toolchain,
                      which generates optimised code for the target platform (e.g.
                      NEST running on a high-performance computing cluster). That
                      code is then dynamically loaded or compiled as part of the
                      simulation framework (in this case, NEST). The model is now
                      ready for use in the simulator, and can be instantiated
                      within a simulation script, written e.g. using the PyNEST
                      API [4], before starting the simulation and performing
                      subsequent analysis.</p><p>NESTML is open sourced under the
                      terms of the GNU General Public License v2.0 and is publicly
                      available at https://github.com/nest/nestml. Extensive
                      documentation and automated testing are in place, both for
                      the language itself as well as the associated processing
                      toolchain. Active user support is provided via the GitHub
                      issue tracker and the NEST user mailing
                      list.</p><h2>References</h2><ol><li>D. Plotnikov et al.
                      (2016) Modellierung March 2-4 2016, Karlsruhe, Germany.
                      93–108. doi:10.5281/zenodo.1412345</li><li>K. Perun et al.
                      (2018). Version 2.4, Zenodo.
                      doi:10.5281/zenodo.1319653</li><li>M.-O. Gewaltig $\&$ M.
                      Diesmann (2007) Scholarpedia 2(4), 1430.
                      doi:10.4249/scholarpedia.1430</li><li>Y.V. Zaytsev $\&$ A.
                      Morrison (2014) Front. Neuroinform. 8:23.
                      doi:10.3389/fninf.2014.00023</li></ol>},
      month         = {Jun},
      date          = {2019-06-24},
      organization  = {NEST Conference 2019: A Forum for
                       Users and Developers, Aas (Norway), 24
                       Jun 2019 - 25 Jun 2019},
      subtyp        = {After Call},
      cin          = {INM-6 / JSC},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)JSC-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907) / SMHB - Supercomputing and Modelling
                      for the Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/865742},
}