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@ARTICLE{Stapmanns:885720,
      author       = {Stapmanns, Jonas and Hahne, Jan and Helias, Moritz and
                      Bolten, Matthias and Diesmann, Markus and Dahmen, David},
      title        = {{E}vent-based update of synapses in voltage-based learning
                      rules},
      reportid     = {FZJ-2020-04032},
      pages        = {arXiv:2009.08667 [q-bio.NC]},
      year         = {2020},
      note         = {42 pages, 12 figures, 7 tables},
      abstract     = {Due to the point-like nature of neuronal spiking, efficient
                      neural network simulators often employ event-based
                      simulation schemes for synapses. Yet many types of synaptic
                      plasticity rely on the membrane potential of the
                      postsynaptic cell as a third factor in addition to pre- and
                      postsynaptic spike times. Synapses therefore require
                      continuous information to update their strength which a
                      priori necessitates a continuous update in a time-driven
                      manner. The latter hinders scaling of simulations to
                      realistic cortical network sizes and relevant time scales
                      for learning. Here, we derive two efficient algorithms for
                      archiving postsynaptic membrane potentials, both compatible
                      with modern simulation engines based on event-based synapse
                      updates. We theoretically contrast the two algorithms with a
                      time-driven synapse update scheme to analyze advantages in
                      terms of memory and computations. We further present a
                      reference implementation in the spiking neural network
                      simulator NEST for two prototypical voltage-based plasticity
                      rules: the Clopath rule and the Urbanczik-Senn rule. For
                      both rules, the two event-based algorithms significantly
                      outperform the time-driven scheme. Depending on the amount
                      of data to be stored for plasticity, which heavily differs
                      between the rules, a strong performance increase can be
                      achieved by compressing or sampling of information on
                      membrane potentials. Our results on computational efficiency
                      related to archiving of information provide guiding
                      principles for the future design of learning rules in order
                      to make them practically usable in large-scale networks.},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
                      modelling and simulation (POF3-574) / MSNN - Theory of
                      multi-scale neuronal networks (HGF-SMHB-2014-2018) /
                      Advanced Computing Architectures $(aca_20190115)$ / HBP SGA2
                      - Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 / $G:(DE-Juel1)aca_20190115$
                      / G:(EU-Grant)785907 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2009.08667},
      howpublished = {arXiv:2009.08667},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2009.08667;\%\%$},
      url          = {https://juser.fz-juelich.de/record/885720},
}