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@INPROCEEDINGS{He:873633,
      author       = {He, Xu and Liu, Tianlin and Hadaeghi, Fatemeh and Jaeger,
                      Herbert},
      title        = {{R}eservoir {T}ransfer on {A}nalog {N}euromorphic
                      {H}ardware},
      reportid     = {FZJ-2020-00876},
      year         = {2019},
      note         = {Tianlin Liu was supported by the FZJ through the project
                      SMARTSTART Computational Neuroscience, DB001423.},
      abstract     = {Analog, unclocked, spiking neuromorphic microchips open new
                      perspectives for implantable or wearable biosensors and
                      biocontrollers, due to their low energy consumption and heat
                      dissipation. However, the challenges from a computational
                      point of view are formidable. Here we outline our solutions
                      to realize the reservoir computing paradigm on such hardware
                      and address the combined problems of low bit resolution,
                      device mismatch, approximate neuron models, and timescale
                      mismatch. The main contribution is a computational scheme,
                      called Reservoir Transfer, which enables us to transfer the
                      dynamical properties of a well-performing neural network
                      which has been optimized on a digital computer, onto
                      neuromorphic hardware that displays the abovementioned
                      problematic properties. Here we present a case study of
                      implementing an ECG heartbeat abnormality detector to
                      showcase the proposed method.},
      month         = {Mar},
      date          = {2019-03-20},
      organization  = {2019 9th International IEEE/EMBS
                       Conference on Neural Engineering (NER),
                       San Francisco (CA), 20 Mar 2019 - 23
                       Mar 2019},
      cin          = {INM-6 / INM-10 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      Smartstart - SMARTSTART Training Program in Computational
                      Neuroscience (90251)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)90251},
      typ          = {PUB:(DE-HGF)1},
      UT           = {WOS:000469933200299},
      doi          = {10.1109/NER.2019.8716891},
      url          = {https://juser.fz-juelich.de/record/873633},
}