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@INPROCEEDINGS{Bouhadjar:907337,
      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 neural network with
                      memristive synapses},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2022-01972},
      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 analog neuromorphic hardware (ANH). It emulates the brain
                      architecture and maps neurons and synapses directly into a
                      physical substrate. Memristive devices have been identified
                      as potential synaptic elements in ANH. 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
                      learning 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 two types of ReRAM
                      devices: (i) an analog switching memristive device, where
                      the conductance gradually changes between a low conductance
                      (LCS) and a high conductance state (HCS), and (ii) a binary
                      switching memristive device, where the conductance abruptly
                      changes between the LCS and the HCS. We study the
                      performance characteristics of the sequence learning model
                      as a function of different device properties, and
                      demonstrate resilience with respect to different on/off
                      ratios, conductance resolutions, device variability, and
                      synaptic failure.},
      month         = {Mar},
      date          = {2022-03-28},
      organization  = {Materials, devices and systems for
                       neuromorphic computing conference,
                       Groningen (Netherlands), 28 Mar 2022 -
                       29 Mar 2022},
      subtyp        = {After Call},
      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) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF4-5232 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)945539 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/907337},
}