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@MISC{Jitsev:186284,
      author       = {Jitsev, Jenia},
      title        = {{N}eural circuitry for learning from reward and punishment},
      reportid     = {FZJ-2015-00368},
      year         = {2014},
      abstract     = {Learning from positive and negative consequences of
                      self-generated behavior is fundamental for securing
                      organism's survival and well-being in uncertain, changing
                      environment. The link between the theory of reinforcement
                      learning that analyzes this kind of behavioral adaptation
                      and the function of the basal ganglia and other brain
                      networks involved in this form of learning belongs to one of
                      the most fruitful within computational neuroscience field.
                      Most fundamental issues, like neural representation of time,
                      states, actions, outcome expectations and reward $\&$
                      punishment itself however are still unresolved. Even the
                      most acclaimed classical finding relating firing of
                      dopaminergic neurons in response to unpredicted rewarding
                      stimuli to prediction error signal postulated in the
                      framework of temporal difference learning is under heavy
                      debate. In the group, we will discuss these basic questions,
                      identifying most crucial challenges, and attempt to sketch a
                      minimal functional neural circuitry that can perform
                      learning from reward and punishment under realistic natural
                      conditions.},
      month         = {Apr},
      date          = {2014-04-27},
      organization  = {CapoCaccia Cognitive Neuromorphic
                       Engineering Workshop, Sardinia (Italy),
                       27 Apr 2014 - 10 May 2014},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / 574 - Theory, modelling and simulation
                      (POF3-574) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / RL-BRD-J - Neural network
                      mechanisms of reinforcement learning (BMBF-01GQ1343)},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-HGF)POF3-574 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-Juel1)BMBF-01GQ1343},
      typ          = {PUB:(DE-HGF)17},
      url          = {https://juser.fz-juelich.de/record/186284},
}