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@INPROCEEDINGS{Bouhadjar:1022054,
      author       = {Bouhadjar, Younes and Wouters, Dirk J. and Diesmann, Markus
                      and Tetzlaff, Tom},
      title        = {{P}robabilistic sequential memory recall in spiking
                      neuronal networks},
      issn         = {1553-734X},
      reportid     = {FZJ-2024-01191},
      year         = {2023},
      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. The model consists of a sparsely
                      connected network of excitatory neurons and a single
                      inhibitory neuron. Excitatory neurons are divided into
                      subpopulations with identical stimulus preferences. External
                      inputs representing specific sequence elements provide
                      neurons in the same subpopulation with identical input. The
                      inhibitory neuron mediates competition between neurons
                      within subpopulations and promotes sparse activity and
                      context sensitivity. Synapses between excitatory neurons are
                      plastic and subject to spike-timing-dependent plasticity and
                      homeostatic control. After learning, the network develops
                      specific subnetworks representing the learned sequences,
                      such that the presentation of a sequence element leads to a
                      context dependent prediction of the subsequent element. 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.},
      month         = {Jul},
      date          = {2023-07-15},
      organization  = {OCNS, Leipzig (Germany), 15 Jul 2023 -
                       19 Jul 2023},
      subtyp        = {After Call},
      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 $(aca_20190115)$ / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      DFG project G:(GEPRIS)491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF4-5232 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1022054},
}