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@ARTICLE{Bouhadjar:908611,
      author       = {Bouhadjar, Younes and Wouters, Dirk J. and Diesmann, Markus
                      and Tetzlaff, Tom},
      title        = {{S}equence learning, prediction, and replay in networks of
                      spiking neurons},
      journal      = {PLoS Computational Biology},
      volume       = {18},
      issn         = {1553-734X},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2022-02720},
      pages        = {e1010233},
      year         = {2022},
      abstract     = {Sequence learning, prediction and replay have been proposed
                      to constitute the universal computations performed by the
                      neocortex. The Hierarchical Temporal Memory (HTM) algorithm
                      realizes these forms of computation. It learns sequences in
                      an unsupervised and continuous manner using local learning
                      rules, permits a context specific prediction of future
                      sequence elements, and generates mismatch signals in case
                      the predictions are not met. While the HTM algorithm
                      accounts for a number of biological features such as
                      topographic receptive fields, nonlinear dendritic
                      processing, and sparse connectivity, it is based on abstract
                      discrete-time neuron and synapse dynamics, as well as on
                      plasticity mechanisms that can only partly be related to
                      known biological mechanisms. Here, we devise a
                      continuous-time implementation of the temporal-memory (TM)
                      component of the HTM algorithm, which is based on a
                      recurrent network of spiking neurons with biophysically
                      interpretable variables and parameters. The model learns
                      high-order sequences by means of a structural Hebbian
                      synaptic plasticity mechanism supplemented with a rate-based
                      homeostatic control. In combination with nonlinear dendritic
                      input integration and local inhibitory feedback, this type
                      of plasticity leads to the dynamic self-organization of
                      narrow sequence-specific subnetworks. These subnetworks
                      provide the substrate for a faithful propagation of sparse,
                      synchronous activity, and, thereby, for a robust, context
                      specific prediction of future sequence elements as well as
                      for the autonomous replay of previously learned sequences.
                      By strengthening the link to biology, our implementation
                      facilitates the evaluation of the TM hypothesis based on
                      experimentally accessible quantities. The continuous-time
                      implementation of the TM algorithm permits, in particular,
                      an investigation of the role of sequence timing for sequence
                      learning, prediction and replay. We demonstrate this aspect
                      by studying the effect of the sequence speed on the sequence
                      learning performance and on the speed of autonomous sequence
                      replay.},
      cin          = {INM-6 / IAS-6 / INM-10 / PGI-7 / PGI-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)PGI-7-20110106 /
                      I:(DE-Juel1)PGI-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 5232 -
                      Computational Principles (POF4-523) / Advanced Computing
                      Architectures $(aca_20190115)$ / PhD no Grant - Doktorand
                      ohne besondere Förderung (PHD-NO-GRANT-20170405) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539) /
                      Open-Access-Publikationskosten Forschungszentrum Jülich
                      (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF4-5232 /
                      $G:(DE-Juel1)aca_20190115$ /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405 / G:(EU-Grant)945539 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35727857},
      UT           = {WOS:000829288500004},
      doi          = {10.1371/journal.pcbi.1010233},
      url          = {https://juser.fz-juelich.de/record/908611},
}