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10.1371/journal.pcbi.1005062 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1005062 C5010205!5010205!27589489     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27589489&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27589489.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+Comput+Biol 2016 ; 12 (9): ä Nephropedia Template TP {{tp|p=27589489|t=2016. Learning Reward Uncertainty in the Basal Ganglia |pdf=|usr=}}{{27589489}} gab.com Text Learning Reward Uncertainty in the Basal Ganglia JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=27589489 #FOAvip Learning the reliability of different sources of rewards is critical for making optimal choices. However, despite the existence of detailed theory describing how the expected reward is learned in the basal ganglia, it is not known how reward uncertainty is estimated in these circuits. This paper presents a class of models that encode both the mean reward and the spread of the rewards, the former in the difference between the synaptic weights of D1 and D2 neurons, and the latter in their sum. In the models, the tendency to seek (or avoid) options with variable reward can be controlled by increasing (or decreasing) the tonic level of dopamine. The models are consistent with the physiology of and synaptic plasticity in the basal ganglia, they explain the effects of dopaminergic manipulations on choices involving risks, and they make multiple experimental predictions. Twit Text FOAVip Learning Reward Uncertainty in the Basal Ganglia ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=27589489 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27589489 #FOAvip English Wikipedia <ref>{{Cite pmid|27589489}}</ref> Learning Reward Uncertainty in the Basal Ganglia #MMPMID27589489 Mikhael JG; Bogacz R PLoS Comput Biol 2016[Sep]; 12 (9): ä PMID27589489show ga Learning the reliability of different sources of rewards is critical for making optimal choices. However, despite the existence of detailed theory describing how the expected reward is learned in the basal ganglia, it is not known how reward uncertainty is estimated in these circuits. This paper presents a class of models that encode both the mean reward and the spread of the rewards, the former in the difference between the synaptic weights of D1 and D2 neurons, and the latter in their sum. In the models, the tendency to seek (or avoid) options with variable reward can be controlled by increasing (or decreasing) the tonic level of dopamine. The models are consistent with the physiology of and synaptic plasticity in the basal ganglia, they explain the effects of dopaminergic manipulations on choices involving risks, and they make multiple experimental predictions. äLearning Reward Uncertainty in the Basal Ganglia (epmc).pdf DeepDyve Pubget Overpricing