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@ARTICLE{Oberlnder:911272,
      author       = {Oberländer, Jette and Bouhadjar, Younes and Morrison,
                      Abigail},
      title        = {{L}earning and replaying spatiotemporal sequences: {A}
                      replication study},
      journal      = {Frontiers in integrative neuroscience},
      volume       = {16},
      issn         = {1662-5145},
      reportid     = {FZJ-2022-04568},
      pages        = {113},
      year         = {2022},
      abstract     = {Learning and replaying spatiotemporal sequences are
                      fundamental computations performed by the brain and
                      specifically the neocortex. These features are critical for
                      a wide variety of cognitive functions, including sensory
                      perception and the execution of motor and language skills.
                      Although several computational models demonstrate this
                      capability, many are either hard to reconcile with
                      biological findings or have limited functionality. To
                      address this gap, a recent study proposed a biologically
                      plausible model based on a spiking recurrent neural network
                      supplemented with read-out neurons. After learning, the
                      recurrent network develops precise switching dynamics by
                      successively activating and deactivating small groups of
                      neurons. The read-out neurons are trained to respond to
                      particular groups and can thereby reproduce the learned
                      sequence. For the model to serve as the basis for further
                      research, it is important to determine its replicability. In
                      this Brief Report, we give a detailed description of the
                      model and identify missing details, inconsistencies or
                      errors in or between the original paper and its reference
                      implementation. We re-implement the full model in the neural
                      simulator NEST in conjunction with the NESTML modeling
                      language and confirm the main findings of the original
                      work.},
      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       = {36310714},
      UT           = {WOS:000876753200001},
      doi          = {10.3389/fnint.2022.974177},
      url          = {https://juser.fz-juelich.de/record/911272},
}