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000186284 037__ $$aFZJ-2015-00368
000186284 1001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b0$$eCorresponding Author$$ufzj
000186284 1112_ $$aCapoCaccia Cognitive Neuromorphic Engineering Workshop$$cSardinia$$d2014-04-27 - 2014-05-10$$wItaly
000186284 245__ $$aNeural circuitry for learning from reward and punishment
000186284 260__ $$c2014
000186284 3367_ $$0PUB:(DE-HGF)17$$2PUB:(DE-HGF)$$aLecture$$blecture$$mlecture$$s1421911025_15941$$xAfter Call
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000186284 520__ $$aLearning 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.
000186284 536__ $$0G:(DE-HGF)POF2-331$$a331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331)$$cPOF2-331$$fPOF II$$x0
000186284 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1
000186284 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000186284 536__ $$0G:(DE-Juel1)BMBF-01GQ1343$$aRL-BRD-J - Neural network mechanisms of reinforcement learning (BMBF-01GQ1343)$$cBMBF-01GQ1343$$x3
000186284 773__ $$y2014
000186284 8564_ $$uhttps://capocaccia.ethz.ch/capo/wiki/2014/learningcircuits14
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000186284 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)158080$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000186284 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000186284 9131_ $$0G:(DE-HGF)POF2-331$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vSignalling Pathways and Mechanisms in the Nervous System$$x0
000186284 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x1
000186284 9141_ $$y2014
000186284 920__ $$lyes
000186284 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000186284 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
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