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10.1038/nrn.2017.7 http://scihub22266oqcxt.onion/10.1038/nrn.2017.7 C5621622!5621622!28209978     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28209978&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 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Rev+Neurosci 2017 ; 18 (3): 172-82 Nephropedia Template TP {{tp|p=28209978|t=2017. A distributed, hierarchical and recurrent framework for reward-based choice |pdf=|usr=}}{{28209978}} gab.com Text A distributed, hierarchical and recurrent framework for reward-based choice JO: Nat Rev Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=28209978 #FOAvip Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organisation of timescales for information processing across the cortex. This account also suggests that certain correlates of value may be emergent rather than represented explicitly in the brain. Twit Text FOAVip A distributed, hierarchical and recurrent framework for reward-based choice ????Nat Rev Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=28209978 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28209978 #FOAvip English Wikipedia <ref>{{Cite pmid|28209978}}</ref> A distributed, hierarchical and recurrent framework for reward-based choice #MMPMID28209978 Hunt LT; Hayden BY Nat Rev Neurosci 2017[Feb]; 18 (3): 172-82 PMID28209978show ga Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organisation of timescales for information processing across the cortex. This account also suggests that certain correlates of value may be emergent rather than represented explicitly in the brain. äA distributed, hierarchical and recurrent framework for reward-based c(epmc).pdf DeepDyve Pubget Overpricing