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@INPROCEEDINGS{Lober:1047535,
      author       = {Lober, Melissa and Bouhadjar, Younes and Neftci, Emre and
                      Diesmann, Markus and Tetzlaff, Tom},
      title        = {{U}nsupervised online learning of complex sequences in
                      spiking neuronal networks},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2025-04365},
      year         = {2025},
      abstract     = {Learning and processing sequential data constitutes a
                      universal form of computation performed by the brain.
                      Understanding the underlying principles does not only shed
                      light on brain function, but also guides the development of
                      energy efficient neuromorphic computing architectures. In a
                      previous study, we devised a spiking recurrent neural
                      network, the spiking temporal memory (spiking TM) model,
                      implementing this type of computation. It learns sequences
                      in a continual, unsupervised manner by means of a local
                      Hebbian synaptic plasticity mechanism. Context specific
                      predictions of upcoming sequence elements are represented by
                      dendritic action potentials. Upon successful learning, the
                      network activity is characterized by a highly sparse and
                      hence energy efficient code. To date, the sequence learning
                      capabilities of the spiking TM model have only been
                      demonstrated for relatively small sequence sets. Here, we
                      systematically investigate the sequence learning capacity of
                      the model by gradually increasing the sequence length and
                      optimizing the plasticity (hyper-) parameters. We show that
                      the spiking TM model at the scale of a few thousand neurons
                      can successfully learn random sequences composed of several
                      tens of elements,with the maximum sequence length exceeding
                      the vocabulary size. After optimizing the plasticity
                      parameters for a given sequence length, the model exhibits
                      high prediction performance for a range of sequence lengths,
                      without additional fine tuning.The learning duration (time
                      to solution) scales supralinearly with the sequence length.
                      Learning longer sequences is hence computationally
                      demanding, and requires accelerated computing
                      architectures.},
      month         = {Jul},
      date          = {2025-07-29},
      organization  = {International Conference on
                       Neuromorphic Systems, Seattle (USA), 29
                       Jul 2025 - 31 Jul 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          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / BMFTR 03ZU2106CB - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - B
                      (BMBF-03ZU2106CB) / BMBF 16ME0398K - Verbundprojekt:
                      Neuro-inspirierte Technologien der künstlichen Intelligenz
                      für die Elektronik der Zukunft - NEUROTEC II -
                      (BMBF-16ME0398K)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 / G:(DE-Juel1)JL
                      SMHB-2021-2027 / G:(DE-Juel1)BMBF-03ZU2106CB /
                      G:(DE-82)BMBF-16ME0398K},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-04365},
      url          = {https://juser.fz-juelich.de/record/1047535},
}