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@ARTICLE{Bouhadjar:908612,
      author       = {Bouhadjar, Younes and Wouters, Dirk J. and Diesmann, Markus
                      and Tetzlaff, Tom},
      title        = {{C}oherent noise enables probabilistic sequence replay in
                      spiking neuronal networks},
      publisher    = {arXiv},
      reportid     = {FZJ-2022-02721},
      year         = {2022},
      abstract     = {Animals rely on different decision strategies when faced
                      with ambiguous or uncertain cues. Depending on the context,
                      decisions may be biased towards events that were most
                      frequently experienced in the past, or be more explorative.
                      A particular type of decision making central to cognition is
                      sequential memory recall in response to ambiguous cues. A
                      previously developed spiking neuronal network implementation
                      of sequence prediction and recall learns complex, high-order
                      sequences in an unsupervised manner by local, biologically
                      inspired plasticity rules. In response to an ambiguous cue,
                      the model deterministically recalls the sequence shown most
                      frequently during training. Here, we present an extension of
                      the model enabling a range of different decision strategies.
                      In this model, explorative behavior is generated by
                      supplying neurons with noise. As the model relies on
                      population encoding, uncorrelated noise averages out, and
                      the recall dynamics remain effectively deterministic. In the
                      presence of locally correlated noise, the averaging effect
                      is avoided without impairing the model performance, and
                      without the need for large noise amplitudes. We investigate
                      two forms of correlated noise occurring in nature: shared
                      synaptic background inputs, and random locking of the
                      stimulus to spatiotemporal oscillations in the network
                      activity. Depending on the noise characteristics, the
                      network adopts various replay strategies. This study thereby
                      provides potential mechanisms explaining how the statistics
                      of learned sequences affect decision making, and how
                      decision strategies can be adjusted after learning.},
      keywords     = {Neurons and Cognition (q-bio.NC) (Other) / FOS: Biological
                      sciences (Other)},
      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 $(aca_20190115)$ / PhD no Grant - Doktorand
                      ohne besondere Förderung (PHD-NO-GRANT-20170405) / 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:(DE-Juel1)PHD-NO-GRANT-20170405 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/arXiv.2206.10538},
      url          = {https://juser.fz-juelich.de/record/908612},
}