TY - JOUR
T1 - Bayesian statistics
T2 - Relevant for the brain?
AU - Kording, Konrad Paul
PY - 2014/4
Y1 - 2014/4
N2 - Analyzing data from experiments involves variables that we neuroscientists are uncertain about. Efficiently calculating with such variables usually requires Bayesian statistics. As it is crucial when analyzing complex data, it seems natural that the brain would "use" such statistics to analyze data from the world. And indeed, recent studies in the areas of perception, action, and cognition suggest that Bayesian behavior is widespread, in many modalities and species. Consequently, many models have suggested that the brain is built on simple Bayesian principles. While the brain's code is probably not actually simple, I believe that Bayesian principles will facilitate the construction of faithful models of the brain.
AB - Analyzing data from experiments involves variables that we neuroscientists are uncertain about. Efficiently calculating with such variables usually requires Bayesian statistics. As it is crucial when analyzing complex data, it seems natural that the brain would "use" such statistics to analyze data from the world. And indeed, recent studies in the areas of perception, action, and cognition suggest that Bayesian behavior is widespread, in many modalities and species. Consequently, many models have suggested that the brain is built on simple Bayesian principles. While the brain's code is probably not actually simple, I believe that Bayesian principles will facilitate the construction of faithful models of the brain.
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U2 - 10.1016/j.conb.2014.01.003
DO - 10.1016/j.conb.2014.01.003
M3 - Review article
C2 - 24463330
AN - SCOPUS:84892868900
SN - 0959-4388
VL - 25
SP - 130
EP - 133
JO - Current opinion in neurobiology
JF - Current opinion in neurobiology
ER -