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@ARTICLE{Skaar:1050703,
      author       = {Skaar, JE. W. and Haug, N. and Plesser, Hans Ekkehard},
      title        = {{A} simplified model of {NMDA}-receptor-mediated dynamics
                      in leaky integrate-and-fire neurons},
      journal      = {Journal of computational neuroscience},
      volume       = {53},
      issn         = {0929-5313},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2026-00448},
      pages        = {475-487},
      year         = {2025},
      abstract     = {A model for NMDA-receptor-mediated synaptic currents in
                      leaky integrate-and-fire neurons, first proposed by Wang (J
                      Neurosci, 1999), has been widely studied in computational
                      neuroscience. The model features a fast rise in the NMDA
                      conductance upon spikes in a pre-synaptic neuron followed by
                      a slow decay. In a general implementation of this model
                      which allows for arbitrary network connectivity and delay
                      distributions, the summed NMDA current from all neurons in a
                      pre-synaptic population cannot be simulated in aggregated
                      form. Simulating each synapse separately is prohibitively
                      slow for all but small networks, which has largely limited
                      the use of the model to fully connected networks with
                      identical delays, for which an efficient simulation scheme
                      exists. We propose an approximation to the original model
                      that can be efficiently simulated for arbitrary network
                      connectivity and delay distributions. Our results
                      demonstrate that the approximation incurs minimal error and
                      preserves network dynamics. We further use the approximate
                      model to explore binary decision making in sparsely coupled
                      networks.},
      cin          = {IAS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / 5234 - Emerging NC
                      Architectures (POF4-523) / 5235 - Digitization of
                      Neuroscience and User-Community Building (POF4-523) / HBP
                      SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(DE-HGF)POF4-5234 / G:(DE-HGF)POF4-5235 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1007/s10827-025-00911-8},
      url          = {https://juser.fz-juelich.de/record/1050703},
}