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@ARTICLE{Wybo:1014283,
      author       = {Wybo, Willem A. M. and Tsai, Matthias C. and Tran, Viet Anh
                      Khoa and Illing, Bernd and Jordan, Jakob and Morrison,
                      Abigail and Senn, Walter},
      title        = {{NMDA}-driven dendritic modulation enables multitask
                      representation learning in hierarchical sensory processing
                      pathways},
      journal      = {Proceedings of the National Academy of Sciences of the
                      United States of America},
      volume       = {120},
      number       = {32},
      issn         = {0027-8424},
      address      = {Washington, DC},
      publisher    = {National Acad. of Sciences},
      reportid     = {FZJ-2023-03213},
      pages        = {e2300558120},
      year         = {2023},
      abstract     = {While sensory representations in the brain depend on
                      context, it remains unclearhow such modulations are
                      implemented at the biophysical level, and how
                      processinglayers further in the hierarchy can extract useful
                      features for each possible contex-tual state. Here, we
                      demonstrate that dendritic N-Methyl-D-Aspartate spikes
                      can,within physiological constraints, implement contextual
                      modulation of feedforwardprocessing. Such neuron-specific
                      modulations exploit prior knowledge, encoded instable
                      feedforward weights, to achieve transfer learning across
                      contexts. In a network ofbiophysically realistic neuron
                      models with context-independent feedforward weights,we show
                      that modulatory inputs to dendritic branches can solve
                      linearly nonseparablelearning problems with a Hebbian,
                      error-modulated learning rule. We also demonstratethat local
                      prediction of whether representations originate either from
                      different inputs,or from different contextual modulations of
                      the same input, results in representationlearning of
                      hierarchical feedforward weights across processing layers
                      that accommodatea multitude of contexts.},
      cin          = {INM-6 / IAS-6 / INM-10 / PGI-15},
      ddc          = {500},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5232 - Computational Principles (POF4-523) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / SDS005 - Towards an integrated data
                      science of complex natural systems (PF-JARA-SDS005) /
                      neuroIC002 - Recurrence and stochasticity for neuro-inspired
                      computation (EXS-SF-neuroIC002) / Functional Neural
                      Architectures $(jinm60_20190501)$},
      pid          = {G:(DE-HGF)POF4-5232 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(DE-Juel-1)PF-JARA-SDS005 / G:(DE-82)EXS-SF-neuroIC002 /
                      $G:(DE-Juel1)jinm60_20190501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37523562},
      UT           = {WOS:001121663700001},
      doi          = {10.1073/pnas.2300558120},
      url          = {https://juser.fz-juelich.de/record/1014283},
}