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@INPROCEEDINGS{Diesmann:1044917,
      author       = {Diesmann, Markus},
      title        = {{N}ew biophysical mechanisms in {NEST}},
      reportid     = {FZJ-2025-03439},
      year         = {2024},
      abstract     = {While the focus of this workshop is on the GPU kernel of
                      NEST in preparation of exascale computers like JUPITER, the
                      extension of the CPU kernel of NEST by further biophysical
                      mechanisms is continuing. In this way, researchers
                      immediately profit from advanced features and the
                      functionality of the CPU code serves as a reference for the
                      GPU implementation. The talk discusses two mechanisms
                      recently added to the CPU code. The first is a framework for
                      neuron-astrocyte interactions. There is presently no
                      consensus on the role of astrocytes in the dynamics and
                      plasticity of neuronal networks. In addition, the equations
                      are complex because the relevance of individual details is
                      unknown. Nevertheless, it is evident now that astrocytes
                      play a role in plasticity, cognition, and behavior.
                      Therefore, the attention of neuroscience to astrocytes is
                      growing and research in the area is expanding. Computational
                      work has so far been restricted to small networks due to the
                      long observation times required and the lack of suitable
                      simulation code. As an example, we discuss a network where
                      astrocytes deliver slow calcium-governed currents (SIC) to
                      postsynaptic neurons. The network exhibits oscillations
                      controlled by neuron-astrocyte interaction. Astrocytes
                      require a generalization of the connectivity concepts of
                      NEST from pairwise rules to tripartite motifs. The framework
                      provides an implementation compatible with the usual hybrid
                      parallelization of the CPU code.The second is a framework
                      for backpropagation-like learning for spiking neuronal
                      networks based on only local information. The theory for
                      this, called eligibility propagation (e-prop), was published
                      by Bellec et al. (2020) together with a time-driven
                      algorithm for TensorFlow. The talk explains the
                      reformulation of the algorithm for the event-driven update
                      of synapses in the NEST code and demonstrates the
                      reproduction of original test cases. This naturally leads to
                      further modifications like the asynchronous update of
                      synaptic weights that maintain learning performance while
                      enhancing biological plausibility. In this way constraints
                      of the original theory are relaxed, and the learning scheme
                      is available for large-scale spiking network models.},
      month         = {Oct},
      date          = {2025-10-23},
      organization  = {NEST GPU Workshop, Cagliari (Italy),
                       23 Oct 2025 - 25 Oct 2025},
      subtyp        = {Invited},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / EBRAINS 2.0 -
                      EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1044917},
}