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@INPROCEEDINGS{Lohoff:1021872,
      author       = {Lohoff, Jamie and Neftci, Emre},
      title        = {{F}inding new bio-plausible {L}earning {R}ules using {D}eep
                      {R}einforcement {L}earning},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2024-01020},
      year         = {2023},
      abstract     = {Gradient-based learning is still the best bet when training
                      spiking neural networks on supervised tasks. Although
                      backpropagation, the state-of-the-art in modern AI, is not
                      bio-plausible, there exists a wide range of approximations
                      with this property that achieve competitive performance,
                      e.g. e-prop[1,2]. We propose a new framework called
                      AlphaGrad that could find more such learning rules by
                      systematically exploring the search space using Deep
                      Reinforcement Learning and methods from Automatic
                      Differentiation(AD).},
      month         = {Oct},
      date          = {2023-10-25},
      organization  = {nternational conference on
                       neuromorphic, natural and physical
                       computing, Hanover (Germany), 25 Oct
                       2023 - 27 Oct 2023},
      subtyp        = {After Call},
      cin          = {PGI-15},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / 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)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
                      G:(DE-82)BMBF-16ME0399},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-01020},
      url          = {https://juser.fz-juelich.de/record/1021872},
}