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@INPROCEEDINGS{Bouhadjar:893728,
      author       = {Bouhadjar, Younes and Diesmann, Markus and Wouters, Dirk J.
                      and Tetzlaff, Tom},
      title        = {{S}equence learning, prediction, and generation in networks
                      of spiking neurons},
      reportid     = {FZJ-2021-02783},
      year         = {2021},
      abstract     = {Sequence learning, prediction and generation has been
                      proposed to be the universal computation performed by the
                      neocortex. The Hierarchical Temporal Memory (HTM) algorithm
                      realizes this form 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 feedforward 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 elementsas
                      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 ofsequence
                      timing for sequence learning, prediction and replay. We
                      demonstrate this aspect by studying the effectof the
                      sequence speed on the sequence learning performance and on
                      the speed of autonomous sequence replay.},
      month         = {Jun},
      date          = {2021-06-28},
      organization  = {NEST Conference, Online (Online), 28
                       Jun 2021 - 29 Jun 2021},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10 / PGI-7 / PGI-10},
      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 $(aca_20190115)$ / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF4-5232 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/893728},
}