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@INPROCEEDINGS{Siegel:1006783,
      author       = {Siegel, Sebastian and Ziegler, Tobias and Bouhadjar, Younes
                      and Tetzlaff, Tom and Waser, Rainer and Dittmann, Regina and
                      Wouters, Dirk},
      title        = {{D}emonstration of neuromorphic sequence learning on a
                      memristive array},
      publisher    = {ACM New York, NY, USA},
      reportid     = {FZJ-2023-01836},
      pages        = {1},
      year         = {2023},
      comment      = {Neuro-Inspired Computational Elements Conference :
                      [Proceedings] - ACM New York, NY, USA, 2023. - ISBN
                      9781450399470 - doi:10.1145/3584954.3585000},
      booktitle     = {Neuro-Inspired Computational Elements
                       Conference : [Proceedings] - ACM New
                       York, NY, USA, 2023. - ISBN
                       9781450399470 -
                       doi:10.1145/3584954.3585000},
      abstract     = {Sequence learning and prediction are considered principle
                      computations performed by biological brains. Machine
                      learning algorithms solve this type of task, but they
                      require large amounts of training data and a substantial
                      energy budget. An approach to overcome these issues and
                      enable sequence learning with brain-like performance is
                      neuromorphic hardware with brain-inspired learning
                      algorithms. The Hierarchical Temporal Memory (HTM) is an
                      algorithm inspired by the working principles of the
                      neocortex and is able to learn and predict continuous
                      sequences of elements. In a previous study, we showed that
                      memristive devices, an emerging non-volatile memory
                      technology, that is considered for energy efficient
                      neuromorphic hardware, can be used as synapses in a
                      biologically plausible version of the temporal memory
                      algorithm of the HTM model. We subsequently presented a
                      simulation study of an analog-mixed signal memristive
                      hardware architecture that can implement the temporal
                      learning algorithm. This architecture, which we refer to as
                      MemSpikingTM, is based on a memristive crossbar array and a
                      control circuitry implementing the neurons and the learning
                      mechanism. In the study presented here, we demonstrate the
                      functionality of the MemSpikingTM algorithm on a real
                      memristive crossbar array, taped out in a commercially
                      available 130nm CMOS technology node co-integrated with HfO
                      based memristive devices. We explain the algorithm and the
                      functionality of the crossbar array and peripheral circuitry
                      and finally demonstrate context-dependent sequence learning
                      using high-order sequences.},
      month         = {Apr},
      date          = {2023-04-03},
      organization  = {NICE 2023: Neuro-Inspired
                       Computational Elements Conference, San
                       Antonio TX USA (USA), 3 Apr 2023 - 7
                       Apr 2023},
      cin          = {PGI-7 / PGI-10 / JARA-FIT / INM-6 / PGI-15},
      cid          = {I:(DE-Juel1)PGI-7-20110106 / I:(DE-Juel1)PGI-10-20170113 /
                      $I:(DE-82)080009_20140620$ / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5233 - Memristive Materials and Devices (POF4-523) / BMBF
                      16ME0399 - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC II - (BMBF-16ME0399) / BMBF 16ME0398K -
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC II - (BMBF-16ME0398K)},
      pid          = {G:(DE-HGF)POF4-5233 / G:(DE-82)BMBF-16ME0399 /
                      G:(DE-82)BMBF-16ME0398K},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001089568500017},
      doi          = {10.1145/3584954.3585000},
      url          = {https://juser.fz-juelich.de/record/1006783},
}