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@ARTICLE{Yang:860292,
      author       = {Yang, Rui and Huang, He-Ming and Hong, Qing-Hui and Yin,
                      Xue-Bing and Tan, Zheng-Hua and Shi, Tuo and Zhou, Ya-Xiong
                      and Miao, Xiang-Shui and Wang, Xiao-Ping and Mi, Shao-Bo and
                      Jia, Chun-Lin and Guo, Xin},
      title        = {{S}ynaptic {S}uppression {T}riplet-{STDP} {L}earning {R}ule
                      {R}ealized in {S}econd-{O}rder {M}emristors},
      journal      = {Advanced functional materials},
      volume       = {28},
      number       = {5},
      issn         = {1616-301X},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {FZJ-2019-01067},
      pages        = {1704455 -},
      year         = {2018},
      abstract     = {The synaptic weight modification depends not only on
                      interval of the pre‐/postspike pairs according to
                      spike‐timing dependent plasticity (classical pair‐STDP),
                      but also on the timing of the preceding spike
                      (triplet‐STDP). Triplet‐STDP reflects the unavoidable
                      interaction of spike pairs in natural spike trains through
                      the short‐term suppression effect of preceding spikes.
                      Second‐order memristors with one state variable possessing
                      short‐term dynamics work in a way similar to the
                      biological system. In this work, the suppression
                      triplet‐STDP learning rule is faithfully demonstrated by
                      experiments and simulations using second‐order memristors.
                      Furthermore, a leaky‐integrate‐and‐fire (LIF) neuron
                      is simulated using a circuit constructed with second‐order
                      memristors. Taking the advantage of the LIF neuron, various
                      neuromimetic dynamic processes, including local graded
                      potential leaking out, postsynaptic impulse generation and
                      backpropagation, and synaptic weight modification according
                      to the suppression triplet‐STDP rule, are realized. The
                      realized weight‐dependent pair‐ and triplet‐STDP rules
                      are clearly in line with findings in biology. The physically
                      realized triplet‐STDP rule is powerful in developing
                      direction and speed selectivity for complex pattern
                      recognition and tracking tasks. These scalable artificial
                      synapses and neurons realized in second‐order memristors
                      can intrinsically capture the neuromimetic dynamic
                      processes; they are the promising building blocks for
                      constructing brain‐inspired computation systems.},
      cin          = {ER-C-1},
      ddc          = {530},
      cid          = {I:(DE-Juel1)ER-C-1-20170209},
      pnm          = {143 - Controlling Configuration-Based Phenomena (POF3-143)},
      pid          = {G:(DE-HGF)POF3-143},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000423512300007},
      doi          = {10.1002/adfm.201704455},
      url          = {https://juser.fz-juelich.de/record/860292},
}