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@ARTICLE{Stapmanns:893117,
      author       = {Stapmanns, Jonas and Hahne, Jan and Helias, Moritz and
                      Bolten, Matthias and Diesmann, Markus and Dahmen, David},
      title        = {{E}vent-{B}ased {U}pdate of {S}ynapses in {V}oltage-{B}ased
                      {L}earning {R}ules},
      journal      = {Frontiers in neuroinformatics},
      volume       = {15},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2021-02574},
      pages        = {609147},
      year         = {2021},
      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. In some learning rules membrane
                      potentials not only influence synaptic weight changes at the
                      time points of spike events but in a continuous manner. In
                      these cases, synapses therefore require information on the
                      full time course of membrane potentials to update their
                      strength which a priori suggests 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 guidelines for the design of learning
                      rules in order to make them practically usable in
                      large-scale networks.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {523 - Neuromorphic Computing and Network Dynamics
                      (POF4-523) / 89574 - Theory, modelling and simulation
                      (POF2-89574) / Advanced Computing Architectures
                      $(aca_20190115)$ / HBP SGA3 - Human Brain Project Specific
                      Grant Agreement 3 (945539) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / MSNN - Theory of
                      multi-scale neuronal networks (HGF-SMHB-2014-2018)},
      pid          = {G:(DE-HGF)POF4-523 / G:(DE-HGF)POF2-89574 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)945539 /
                      G:(EU-Grant)785907 / G:(DE-Juel1)HGF-SMHB-2014-2018},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34177505},
      UT           = {WOS:000664997900001},
      doi          = {10.3389/fninf.2021.609147},
      url          = {https://juser.fz-juelich.de/record/893117},
}