Neural Implementation of Bayesian Inference in Population Codes

@misc{citeulike:2805630, abstract = {This study investigates a population decoding paradigm, in which the estimation of stimulus in the previous step is used as prior knowledge for consecutive decoding. We analyze the decoding accuracy of such a Bayesian decoder (Maximum a Posteriori Estimate), and show that it can be implemented by a biologically plausible recurrent network, where the prior knowledge of stimulus is conveyed by the change in recurrent interactions as a result of Hebbian learning.}, author = {Wu, Si and Amari, Shun I. }, citeulike-article-id = {2805630}, keywords = {bayesian, population-coding}, posted-at = {2008-05-16 18:43:38}, priority = {2}, title = {Neural Implementation of Bayesian Inference in Population Codes}, url = {http://citeseer.ist.psu.edu/541794.html} }

TY - GEN ID - citeulike:2805630 L3 - citeulike-article-id:2805630 TI - Neural Implementation of Bayesian Inference in Population Codes N2 - This study investigates a population decoding paradigm, in which the estimation of stimulus in the previous step is used as prior knowledge for consecutive decoding. We analyze the decoding accuracy of such a Bayesian decoder (Maximum a Posteriori Estimate), and show that it can be implemented by a biologically plausible recurrent network, where the prior knowledge of stimulus is conveyed by the change in recurrent interactions as a result of Hebbian learning. KW - bayesian KW - population-coding AU - Wu, Si AU - Amari, Shun UR - http://citeseer.ist.psu.edu/541794.html ER -