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@INPROCEEDINGS{Feiler:1037900,
      author       = {Feiler, Florian and Neftci, Emre and Bouhadjar, Younes},
      title        = {{U}nsupervised {L}earning of {S}patio-{T}emporal {P}atterns
                      in {S}piking {N}euronal {N}etworks},
      reportid     = {FZJ-2025-01038},
      pages        = {366 - 370},
      year         = {2024},
      abstract     = {The ability to predict future events or patterns based on
                      previous experience is crucial for many applications such as
                      traffic control, weather forecasting, or supply chain
                      management. While modern supervised Machine Learning
                      approaches excel at such sequential tasks, they are
                      computationally expensive and require large training data. A
                      previous work presented a biologically plausible sequence
                      learning model, developed through a bottom-up approach,
                      consisting of a spiking neural network and unsupervised
                      local learning rules. The model in its original formulation
                      identifies only a specific type of sequence elements
                      composed of synchronous spikes by activating a subset of
                      neurons with identical stimulus preference. In this work, we
                      extend the model to detect and learn sequences of various
                      spatio-temporal patterns (STPs) by incorporating plastic
                      connections in the input synapses. We showcase that the
                      model is able to learn and predict high-order sequences. We
                      further study the robustness of the model against different
                      input settings and parameters.},
      month         = {Jul},
      date          = {2024-07-30},
      organization  = {International Conference on
                       Neuromorphic Systems (ICONS),
                       Arlington, Virginia (USA), 30 Jul 2024
                       - 2 Aug 2024},
      cin          = {PGI-15 / PGI-7},
      cid          = {I:(DE-Juel1)PGI-15-20210701 / I:(DE-Juel1)PGI-7-20110106},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / BMBF
                      16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC II - (BMBF-16ME0398K) / BMBF 16ME0399 -
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC II - (BMBF-16ME0399)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
                      G:(DE-82)BMBF-16ME0399},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/1037900},
}