% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Linssen:909578,
      author       = {Linssen, Charl and Korcsak-Gorzo, Agnes and Albers, Jasper
                      and Babu, Pooja and Böttcher, Joshua and Mitchell, Jessica
                      and Wybo, Willem and Bruchertseifer, Jens and Spreizer,
                      Sebastian and Terhorst, Dennis},
      title        = {{F}rom single-cell modeling to large-scale network dynamics
                      with {NEST} {S}imulator},
      reportid     = {FZJ-2022-03260},
      year         = {2022},
      abstract     = {NEST is an established, open-source simulator for spiking
                      neuronal networks, which can capture a high degree of detail
                      of biological network structures while retaining high
                      performance and scalability from laptops to HPC [1]. This
                      tutorial provides hands-on experience in building and
                      simulating neuron, synapse, and network models. It
                      introduces several tools and front-ends to implement
                      modeling ideas most efficiently. Participants do not have to
                      install software as all tools can be accessed via the
                      cloud.First, we look at NEST Desktop [2], a web-based
                      graphical user interface (GUI), which allows the exploration
                      of essential concepts in computational neuroscience without
                      the need to learn a programming language. This advances both
                      the quality and speed of teaching in computational
                      neuroscience. To get acquainted with the GUI, we will create
                      and analyze abalanced two-population network.The model is
                      then exported to a Jupyter notebook and endowed with a
                      data-driven spatial connectivity profile of the cortex,
                      enabling us to study the propagation of activity. Then, we
                      make the synapses in the network plastic and let the network
                      learn a reinforcement learning task, whereby the learning
                      rule goes beyond pre-synaptic and post-synaptic spikes by
                      addinga dopamine signal as a modulatory third factor. NESTML
                      [3] makes it easy to express this and other advanced
                      synaptic plasticity rules and neuron models, and
                      automatically translates them into fast simulation code.More
                      morphologically detailed models, with a large number of
                      compartments and custom ion channels and receptor currents,
                      can also be defined using NESTML. We first implement a
                      simple dendritic layout and use it to perform a sequence
                      discrimination task. Next, we implement a compartmental
                      layout representing semi-independent subunits and
                      recurrentlyconnect several such neurons to elicit an
                      NMDA-spike driven network state.},
      month         = {Jul},
      date          = {2022-07-16},
      organization  = {31st Annual Computational Neuroscience
                       Meeting, CNS*2022, Melbourne
                       (Australia), 16 Jul 2022 - 20 Jul 2022},
      subtyp        = {Invited},
      cin          = {INM-6 / INM-10 / IAS-6 / JSC},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)JSC-20090406},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / 5231 - Neuroscientific Foundations
                      (POF4-523) / 5234 - Emerging NC Architectures (POF4-523) /
                      5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HBP SGA3 - Human
                      Brain Project Specific Grant Agreement 3 (945539) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(DE-HGF)POF4-5231 /
                      G:(DE-HGF)POF4-5234 / G:(DE-HGF)POF4-5111 /
                      G:(EU-Grant)945539 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/909578},
}