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| Hauptseite > Publikationsdatenbank > Neural circuitry for learning from reward and punishment |
| Hauptseite > Publikationsdatenbank > Neural circuitry for learning from reward and punishment |
| Lecture (After Call) | FZJ-2015-00368 |
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.
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