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@INPROCEEDINGS{Lober:1041699,
      author       = {Lober, Melissa and Bouhadjar, Younes and Diesmann, Markus
                      and Tetzlaff, Tom},
      title        = {{L}earning sequence timing and controlling recall speed in
                      networks of spiking neurons},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2025-02386},
      year         = {2025},
      abstract     = {Processing sequential inputs is a fundamental aspect of
                      brain function, underlying tasks such as sensory perception,
                      reading, and mathematical reasoning. At the core of the
                      cortical algorithm, sequence processing involves learning
                      the order and timing of elements, predicting future events,
                      detecting unexpected deviations, and recalling learned
                      sequences. The Spiking Temporal Memory (STM) model [1], a
                      biologically inspired spiking neuronal network, provides a
                      framework for key aspects of sequence processing. In its
                      original version, however, it can not learn the timing of
                      sequence elements. Further, it remains an open question how
                      the speed of sequential recall can be flexibly modulated. We
                      propose a mechanism in which the duration of sequence
                      elements is represented by repeated activations of element
                      specific neuronal populations. The STM model can thereby
                      represent even long time intervals, providing a biologically
                      plausible basis for learning and recalling not only the
                      order of sequence elements, but also complex
                      rhythms.Additionally, we demonstrate that oscillatory
                      background inputs can serve as a clock signal and thereby
                      provide a robust mechanism for controlling the speed of
                      sequence recall. Modulation of oscillation frequency and
                      amplitude enable a stable recall across a wide range of
                      speeds, offering a biologically relevant strategy for
                      flexible temporal adaptation.Our findings suggest that time
                      is encoded by unique and sparse spatio-temporal patterns of
                      neural activity, and that the speed of sequence recall is
                      correlated to the characteristics of global oscillatory
                      activity, observed in EEG or LFP recordings. In summary, our
                      results contribute to the understanding of sequence
                      processing and time representation in the brain.[1]
                      Bouhadjar, Y., Wouters, D. J., Diesmann, M., Tetzlaff, T.
                      (2022), Sequence learning, prediction, and replay in
                      networks of spiking neurons, PLOS Computational Biology
                      18(6):e1010233},
      month         = {May},
      date          = {2025-05-27},
      organization  = {IAS Retreat, Juelich (Germany), 27 May
                       2025 - 27 May 2025},
      subtyp        = {After Call},
      cin          = {IAS-6 / PGI-15 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)PGI-15-20210701 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / 5231 -
                      Neuroscientific Foundations (POF4-523) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5231 / G:(DE-Juel1)JL
                      SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1041699},
}