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@INPROCEEDINGS{Wybo:1008841,
      author       = {Wybo, Willem and Tran, Viet Anh Khoa and Tsai, Matthias and
                      Illing, Bernd and Jordan, Jakob and Senn, Walter and
                      Morrison, Abigail},
      title        = {{D}endritic modulation for multitask representation
                      learning in deep feedforward networks},
      reportid     = {FZJ-2023-02506},
      year         = {2023},
      abstract     = {Feedforward sensory processing in the brain is generally
                      construed as proceeding through a hierar- chy of layers,
                      each constructing increasingly abstract and invariant
                      representations of sensory inputs. This interpretation is at
                      odds with the observation that activity in sensory
                      processing layers is heavily modulated by contextual
                      signals, such as cross modal information or internal mental
                      states [1]. While it is tempting to assume that such
                      modulations bias the feedforward processing pathway towards
                      de- tection of relevant input features given a context, this
                      induces a dependence on the contextual state in hidden
                      representations at any given layer. The next processing
                      layer in the hierarchy thus has to be able to extract
                      relevant information for each possible context. For this
                      reason, most machine learning approaches to multitask
                      learning apply task-specific output networks to
                      context-independent representations of the inputs, generated
                      by a shared trunk network.Here, we show that a network
                      motif, where a layer of modulated hidden neurons targets an
                      out- put neuron through task-independent feedforward
                      weights, solves multitask learning problems, and that this
                      network motif can be implemented with biophysically
                      realistic neurons that receive context- modulating synaptic
                      inputs on dendritic branches. The dendritic synapses in this
                      motif evolve ac- cording to a Hebbian plasticity rule
                      modulated by a global error signal. We then embed such a
                      motif in each layer of a deep feedforward network, where it
                      generates task-modulated representations of sensory inputs.
                      To learn feedforward weights to the next layer in the
                      network, we apply a contrastive learning objective that
                      predicts whether representations originate either from
                      different inputs, or from different task-modulations of the
                      same input. This self-supervised approach results in deep
                      represen- tation learning of feedforward weights that
                      accommodate a multitude of contexts, without relying on
                      error backpropagation between layers.},
      month         = {Mar},
      date          = {2023-03-08},
      organization  = {Cosyne 2023, Montreal (Canada), 8 Mar
                       2023 - 16 Mar 2023},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      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)},
      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},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-02506},
      url          = {https://juser.fz-juelich.de/record/1008841},
}