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@PHDTHESIS{Bouhadjar:1006807,
      author       = {Bouhadjar, Younes},
      title        = {{A} brain inspired sequence learning algorithm and
                      foundations of a memristive hardware implementation},
      volume       = {95},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2023-01860},
      isbn         = {978-3-95806-693-9},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {xii, 149},
      year         = {2023},
      note         = {Dissertation, RWTH Aachen University, 2023},
      abstract     = {The brain uses intricate biological mechanisms and
                      principles to solve a variety of tasks. These principles
                      endow systems with self-learning capabilities, efficient
                      energy usage, and high storage capacity. A core concept that
                      lies at the heart of brain computation is sequence
                      prediction and replay. This form of computation is essential
                      for almost all our daily tasks such as movement generation,
                      perception, and language. Understanding how the brain
                      performs such a computation advances neuroscience and paves
                      the way for new technological brain-inspired applications.
                      In the first part of this thesis, we propose a sequence
                      learning model that explains how biological networks learn
                      to predict upcoming elements, signal non-anticipated events,
                      and recall sequences in response to a cue signal. The model
                      accounts for anatomical and electrophysiological properties
                      of cortical neuronal circuits, and learns complex sequences
                      in an unsupervised manner by means of known biological
                      plasticity and homeostatic control mechanisms. After
                      learning, it self-organizes into a configuration
                      characterized by a high degree of sparsity in connectivity
                      and activity allowing for both high storage capacity and
                      efficient energy usage. In the second part, we extend the
                      sequence learning model such that it permits probabilistic
                      sequential memory recall in response to ambiguous cues. In
                      the absence of noise, the model deterministically recalls
                      the sequence shown most frequently during training. We
                      investigate how different forms of noise give rise to more
                      exploratory behavior. We show that uncorrelated noise
                      averages out in population based encoding leading to
                      non-exploratory dynamics. Locally coherent noise in the form
                      of random stimulus locking to spatiotemporal oscillations
                      addresses this issue. Our results show that depending on the
                      amplitude and frequency of oscillation, the network can
                      recall learned sequences according to different strategies:
                      either always replay the most frequent sequence, or replay
                      sequences according to their occurrence probability during
                      training. The study contributes to an understanding of the
                      neuronal mechanisms underlying different decision strategies
                      in the face of ambiguity, and highlights the role of
                      coherent network activity during sequential memory recall.
                      Finally, we investigate the feasibility of implementing the
                      sequence learning model on dedicated hardware mimicking
                      brain properties. Here, we focus on a type of hardware where
                      synapses are emulated by memristive devices. As a first step
                      in this direction, we replace the synapse dynamics of the
                      original model with dynamics describing the phenomenological
                      behavior of memristive elements, and demonstrate resilience
                      with respect to different device characteristics. In this
                      thesis, we further describe how the sequence learning model
                      can be adapted at the algorithmic level to foster an
                      implementation in a full electronic circuit centered around
                      a memristive crossbar array. Overall, this thesis sheds
                      light on the key mechanisms underlying sequence learning,
                      prediction, and replay in biological networks and
                      demonstrates the feasibility of implementing this type of
                      computation on neuromorphic hardware.},
      cin          = {INM-6 / IAS-6 / INM-10 / PGI-10 / PGI-7},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)PGI-10-20170113 /
                      I:(DE-Juel1)PGI-7-20110106},
      pnm          = {5232 - Computational Principles (POF4-523) / 574 - Theory,
                      modelling and simulation (POF3-574) / ACA - Advanced
                      Computing Architectures (SO-092) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      DFG project 491111487 - Open-Access-Publikationskosten /
                      2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ)
                      (491111487)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF3-574 / G:(DE-HGF)SO-092
                      / G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-20230718092150862-6422765-5},
      url          = {https://juser.fz-juelich.de/record/1006807},
}