Hauptseite > Publikationsdatenbank > Neural circuitry for learning from reward and punishment > print

001     186284
005     20240313094953.0
037 _ _ |a FZJ-2015-00368
100 1 _ |a Jitsev, Jenia
|0 P:(DE-Juel1)158080
|b 0
|e Corresponding Author
|u fzj
111 2 _ |a CapoCaccia Cognitive Neuromorphic Engineering Workshop
|c Sardinia
|d 2014-04-27 - 2014-05-10
|w Italy
245 _ _ |a Neural circuitry for learning from reward and punishment
260 _ _ |c 2014
336 7 _ |a Lecture
|b lecture
|m lecture
|0 PUB:(DE-HGF)17
|s 1421911025_15941
|2 PUB:(DE-HGF)
|x After Call
336 7 _ |a Text
|2 DataCite
336 7 _ |a MISC
|2 BibTeX
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Generic
|0 31
|2 EndNote
336 7 _ |a lecture
|2 DRIVER
520 _ _ |a 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.
536 _ _ |a 331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331)
|0 G:(DE-HGF)POF2-331
|c POF2-331
|f POF II
|x 0
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 1
536 _ _ |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)
|0 G:(DE-Juel1)HGF-SMHB-2013-2017
|c HGF-SMHB-2013-2017
|f SMHB
|x 2
536 _ _ |a RL-BRD-J - Neural network mechanisms of reinforcement learning (BMBF-01GQ1343)
|0 G:(DE-Juel1)BMBF-01GQ1343
|c BMBF-01GQ1343
|x 3
773 _ _ |y 2014
856 4 _ |u https://capocaccia.ethz.ch/capo/wiki/2014/learningcircuits14
909 C O |o oai:juser.fz-juelich.de:186284
|p VDB
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)158080
913 2 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
|2 G:(DE-HGF)POF3-500
|v Theory, modelling and simulation
|x 0
913 1 _ |a DE-HGF
|b Gesundheit
|l Funktion und Dysfunktion des Nervensystems
|1 G:(DE-HGF)POF2-330
|0 G:(DE-HGF)POF2-331
|2 G:(DE-HGF)POF2-300
|v Signalling Pathways and Mechanisms in the Nervous System
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF2
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
|2 G:(DE-HGF)POF3-500
|v Theory, modelling and simulation
|x 1
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2014
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
|k INM-6
|l Computational and Systems Neuroscience
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
|k IAS-6
|l Theoretical Neuroscience
|x 1
980 _ _ |a lecture
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)IAS-6-20130828
981 _ _ |a I:(DE-Juel1)IAS-6-20130828

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