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@ARTICLE{Weidel:890994,
      author       = {Weidel, Philipp and Duarte, Renato and Morrison, Abigail},
      title        = {{U}nsupervised {L}earning and {C}lustered {C}onnectivity
                      {E}nhance {R}einforcement {L}earning in {S}piking {N}eural
                      {N}etworks},
      journal      = {Frontiers in computational neuroscience},
      volume       = {15},
      issn         = {1662-5188},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2021-01301},
      pages        = {543872},
      year         = {2021},
      abstract     = {Reinforcement learning is a paradigm that can account for
                      how organisms learn to adapt their behavior in complex
                      environments with sparse rewards. To partition an
                      environment into discrete states, implementations in spiking
                      neuronal networks typically rely on input architectures
                      involving place cells or receptive fields specified ad hoc
                      by the researcher. This is problematic as a model for how an
                      organism can learn appropriate behavioral sequences in
                      unknown environments, as it fails to account for the
                      unsupervised and self-organized nature of the required
                      representations. Additionally, this approach presupposes
                      knowledge on the part of the researcher on how the
                      environment should be partitioned and represented and scales
                      poorly with the size or complexity of the environment. To
                      address these issues and gain insights into how the brain
                      generates its own task-relevant mappings, we propose a
                      learning architecture that combines unsupervised learning on
                      the input projections with biologically motivated clustered
                      connectivity within the representation layer. This
                      combination allows input features to be mapped to clusters;
                      thus the network self-organizes to produce clearly
                      distinguishable activity patterns that can serve as the
                      basis for reinforcement learning on the output projections.
                      On the basis of the MNIST and Mountain Car tasks, we show
                      that our proposed model performs better than either a
                      comparable unclustered network or a clustered network with
                      static input projections. We conclude that the combination
                      of unsupervised learning and clustered connectivity provides
                      a generic representational substrate suitable for further
                      computation.},
      cin          = {INM-6 / IAS-6 / INM-10 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / $I:(DE-82)080012_20140620$},
      pnm          = {523 - Neuromorphic Computing and Network Dynamics
                      (POF4-523) / 89574 - Theory, modelling and simulation
                      (POF2-89574) / Functional Neural Architectures
                      $(jinm60_20190501)$ / 5232 - Computational Principles
                      (POF4-523)},
      pid          = {G:(DE-HGF)POF4-523 / G:(DE-HGF)POF2-89574 /
                      $G:(DE-Juel1)jinm60_20190501$ / G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33746728},
      UT           = {WOS:000629977000001},
      doi          = {10.3389/fncom.2021.543872},
      url          = {https://juser.fz-juelich.de/record/890994},
}