caa a22 4500 445295082 CHVBK 20180317142539.0 cr unu---uuuuu 170323e20100601xx s 000 0 eng 10.1007/s11571-010-9109-x doi (NATIONALLICENCE)springer-10.1007/s11571-010-9109-x Computational models of reinforcement learning: the role of dopamine as a reward signal [Elektronische Daten] [R. Samson, M. Frank, Jean-Marc Fellous] Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success. Springer Science+Business Media B.V., 2010 Reinforcement learning nationallicence Dopamine nationallicence Reward nationallicence Temporal difference nationallicence Samson R. Evelyn F. McKnight Brain Institute and Neural Systems, Memory and Aging, University of Arizona, 85724, Tucson, AZ, USA aut Frank M. Department of Cognitive and Linguistic Sciences and Department of Psychology, Brown Institute for Brain Science, Brown University, 02912, Providence, RI, USA aut Fellous Jean-Marc Department of Psychology and Applied Mathematics, University of Arizona, 1501 N. Campbell Av, Life Sciences North, #384, 85724, Tucson, AZ, USA aut Cognitive Neurodynamics Springer Netherlands 4/2(2010-06-01), 91-105 1871-4080 4:2<91 2010 4 11571 https://doi.org/10.1007/s11571-010-9109-x text/html Onlinezugriff via DOI 1 review-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11571-010-9109-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Samson R. Evelyn F. McKnight Brain Institute and Neural Systems, Memory and Aging, University of Arizona, 85724, Tucson, AZ, USA aut NATIONALLICENCE 700 1- Frank M. Department of Cognitive and Linguistic Sciences and Department of Psychology, Brown Institute for Brain Science, Brown University, 02912, Providence, RI, USA aut NATIONALLICENCE 700 1- Fellous Jean-Marc Department of Psychology and Applied Mathematics, University of Arizona, 1501 N. Campbell Av, Life Sciences North, #384, 85724, Tucson, AZ, USA aut NATIONALLICENCE 773 0- Cognitive Neurodynamics Springer Netherlands 4/2(2010-06-01), 91-105 1871-4080 4:2<91 2010 4 11571 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer