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@ARTICLE{Bouhadjar:916364,
      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},
      publisher    = {ArXiv},
      reportid     = {FZJ-2022-06165},
      year         = {2022},
      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
                      holds promise for efficiently running 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 simulator
                      NEST. We investigate the effect of different device
                      properties on the performance characteristics of the
                      sequence learning model, and demonstrate resilience with
                      respect to different on-off ratios, conductance resolutions,
                      device variability, and synaptic failure.},
      keywords     = {Neural and Evolutionary Computing (cs.NE) (Other) /
                      Emerging Technologies (cs.ET) (Other) / FOS: Computer and
                      information sciences (Other)},
      cin          = {INM-6 / IAS-6 / INM-10 / PGI-7 / PGI-10},
      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},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 5232 -
                      Computational Principles (POF4-523) / Advanced Computing
                      Architectures $(aca_20190115)$ / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF4-5232 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/arXiv.2211.16592},
      url          = {https://juser.fz-juelich.de/record/916364},
}