Bayesian inference with probabilistic population codes Export

@article{citeulike:913189, abstract = {Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.}, author = {Ma, Wei J. and Beck, Jeffrey M. and Latham, Peter E. and Pouget, Alexandre}, citeulike-article-id = {913189}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nn1790}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nn1790}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17057707}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17057707}, day = {22}, doi = {10.1038/nn1790}, issn = {1097-6256}, journal = {Nature Neuroscience}, keywords = {bayesian, glm, population}, month = {October}, number = {11}, pages = {1432--1438}, posted-at = {2009-03-17 12:50:41}, priority = {2}, publisher = {Nature Publishing Group}, title = {Bayesian inference with probabilistic population codes}, url = {http://dx.doi.org/10.1038/nn1790}, volume = {9}, year = {2006} }

TY - JOUR ID - citeulike:913189 L3 - citeulike-article-id:913189 N2 - Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability. IS - 11 JF - Nature Neuroscience SN - 1097-6256 EP - 1438 TI - Bayesian inference with probabilistic population codes PB - Nature Publishing Group VL - 9 SP - 1432 KW - bayesian KW - glm KW - population AU - Ma, Wei AU - Beck, Jeffrey AU - Latham, Peter AU - Pouget, Alexandre PY - 2006/10/22/ UR - http://dx.doi.org/10.1038/nn1790 ER -