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@INPROCEEDINGS{Spreizer:1032427,
      author       = {Spreizer, Sebastian and Linssen, Charl and Babu, Pooja and
                      Diesmann, Markus and Morrison, Abigail and Weyers, Benjamin},
      title        = {{R}apid prototyping in spiking neural network modeling with
                      {NESTML} and {NEST} {D}esktop},
      reportid     = {FZJ-2024-06235},
      year         = {2024},
      abstract     = {NEST [1] is a well-established open source simulator
                      providing researchers in computational neuroscience with the
                      ability to perform high-performance simulations of spiking
                      neuronal networks. However, as the simulation kernel is
                      written in C++ for performance reasons, this makes it
                      challenging for researchers without a programming background
                      to customize and extend the built-in neuron and synapse
                      models.In order to satisfy both the need for
                      high-performance simulation codes and a good user experience
                      in terms of easy-to-use modeling of neurons and synapses,
                      NESTML [2] was created as a domain-specific language with an
                      unambiguous syntax. Alongside the language itself, a
                      toolchain was developed that parses the model, analyzes the
                      underlying equations, and performs code generation. The code
                      generated by NESTML can be used in simulations of brain
                      activity on several platforms, in particular the NEST
                      Simulator. However, specification of the network
                      architecture requires using the NEST Python API, which still
                      requires users to be skilled at programming. This poses a
                      problem for beginners, as experience shows that they usually
                      have little to no coding experience.This issue was addressed
                      by the development of NEST Desktop [3], a graphical user
                      interface that serves as an intuitive, programming-free
                      interface to NEST. The interface is easily installed and
                      accessed via an internet browser on the computers of
                      individual users, or in cloud-based deployments, which have
                      already been proven in the field in student courses at
                      universities across Europe.Here, we demonstrate for the
                      first time the integration of all three components: NEST
                      Desktop, NESTML and NEST. As a result, researchers and
                      students can customize existing models or develop new ones
                      using NESTML, and have them instantly available to create
                      networks using the graphical interface of NEST Desktop,
                      before simulating them efficiently using NEST as a back-end.
                      The combined strength of these components creates a
                      low-barrier environment for rapid prototyping and
                      exploration of neuron, synapse and network
                      models.<br><br>References<br>NEST (Neural Simulation Tool),
                      Gewaltig M-O $\&$ Diesmann M (2007),
                      10.4249/scholarpedia.1430 <br>NESTML 7.0.1, Linssen C et al.
                      (2024), 10.5281/zenodo.10966350 <br>NEST Desktop, Spreizer
                      et al. (2021), 10.1523/eneuro.0274-21.2021},
      month         = {Sep},
      date          = {2024-09-29},
      organization  = {Bernstein Conference 2024, Frankfurt
                       (Germany), 29 Sep 2024 - 2 Oct 2024},
      subtyp        = {After Call},
      cin          = {JSC / IAS-6},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5234 - Emerging NC
                      Architectures (POF4-523) / 5235 - Digitization of
                      Neuroscience and User-Community Building (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / PhD no Grant - Doktorand
                      ohne besondere Förderung (PHD-NO-GRANT-20170405) / HBP SGA2
                      - Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / ACA - Advanced Computing Architectures (SO-092)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5234 /
                      G:(DE-HGF)POF4-5235 / G:(DE-HGF)POF4-5232 /
                      G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)SO-092},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1032427},
}