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@ARTICLE{Zajzon:1008686,
      author       = {Zajzon, Barna and Duarte, Renato and Morrison, Abigail},
      title        = {{T}oward reproducible models of sequence learning:
                      replication and analysis of a modular spiking network with
                      reward-based learning},
      journal      = {Frontiers in integrative neuroscience},
      volume       = {17},
      issn         = {1662-5145},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2023-02477},
      pages        = {935177},
      year         = {2023},
      abstract     = {To acquire statistical regularities from the world, the
                      brain must reliably process, and learn from,
                      spatio-temporally structured information. Although an
                      increasing number of computational models have attempted to
                      explain how such sequence learning may be implemented in the
                      neural hardware, many remain limited in functionality or
                      lack biophysical plausibility. If we are to harvest the
                      knowledge within these models and arrive at a deeper
                      mechanistic understanding of sequential processing in
                      cortical circuits, it is critical that the models and their
                      findings are accessible, reproducible, and quantitatively
                      comparable. Here we illustrate the importance of these
                      aspects by providing a thorough investigation of a recently
                      proposed sequence learning model. We re-implement the
                      modular columnar architecture and reward-based learning rule
                      in the open-source NEST simulator, and successfully
                      replicate the main findings of the original study. Building
                      on these, we perform an in-depth analysis of the model's
                      robustness to parameter settings and underlying assumptions,
                      highlighting its strengths and weaknesses. We demonstrate a
                      limitation of the model consisting in the hard-wiring of the
                      sequence order in the connectivity patterns, and suggest
                      possible solutions. Finally, we show that the core
                      functionality of the model is retained under more
                      biologically-plausible constraints},
      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          = {5232 - Computational Principles (POF4-523) / neuroIC002 -
                      Recurrence and stochasticity for neuro-inspired computation
                      (EXS-SF-neuroIC002) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-82)EXS-SF-neuroIC002 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37396571},
      UT           = {WOS:001018401100001},
      doi          = {10.3389/fnint.2023.935177},
      url          = {https://juser.fz-juelich.de/record/1008686},
}