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@ARTICLE{Bouhadjar:1017446,
      author       = {Bouhadjar, Younes and Siegel, Sebastian and Tetzlaff, Tom
                      and Diesmann, Markus and Waser, R. and Wouters, Dirk J.},
      title        = {{S}equence learning in a spiking neuronal network with
                      memristive synapses},
      journal      = {Neuromorphic computing and engineering},
      volume       = {3},
      issn         = {2634-4386},
      publisher    = {IOP Publishing Ltd.},
      reportid     = {FZJ-2023-04142},
      pages        = {034014},
      year         = {2023},
      abstract     = {Brain-inspired computing proposes a set of algorithmic
                      principles that hold promise for advancing artificial
                      intelligence. They 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 learning and prediction. 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 is
                      not only important to advance neuroscience, but also to pave
                      the way to new technological brain-inspired applications. A
                      previously developed spiking neural network implementation
                      of sequence prediction and recall learns complex, high-order
                      sequences in an unsupervised manner by local, biologically
                      inspired plasticity rules. An emerging type of hardware that
                      may efficiently run this type of algorithm is neuromorphic
                      hardware. It emulates the way the brain processes
                      information and maps neurons and synapses directly into a
                      physical substrate. Memristive devices have been identified
                      as potential synaptic elements in neuromorphic hardware. In
                      particular, redox-induced resistive random access memories
                      (ReRAM) devices stand out at many aspects. They permit
                      scalability, are energy efficient and fast, and can
                      implement biological plasticity rules. In this work, we
                      study the feasibility of using ReRAM devices as a
                      replacement of the biological synapses in the sequence
                      learning model. We implement and simulate the model
                      including the ReRAM plasticity using the neural network
                      simulator NEST. We investigate two types of ReRAM memristive
                      devices: (i) a gradual, analog switching device, and (ii) an
                      abrupt, binary switching device. We study the effect of
                      different device properties on the performance
                      characteristics of the sequence learning model, and
                      demonstrate that, in contrast to many other artificial
                      neural networks, this architecture is resilient with respect
                      to changes in the on-off ratio and the conductance
                      resolution, device variability, and device failure.},
      cin          = {INM-6 / IAS-6 / INM-10 / PGI-7 / PGI-10 / PGI-15},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)PGI-7-20110106 /
                      I:(DE-Juel1)PGI-10-20170113 / I:(DE-Juel1)PGI-15-20210701},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 5232 -
                      Computational Principles (POF4-523) / Advanced Computing
                      Architectures $(aca_20190115)$ / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      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) / DFG
                      project 491111487 - Open-Access-Publikationskosten / 2022 -
                      2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF4-5232 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-82)BMBF-16ME0398K /
                      G:(DE-82)BMBF-16ME0399 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1088/2634-4386/acf1c4},
      url          = {https://juser.fz-juelich.de/record/1017446},
}