Bayesian Brain

Recent New Scientist article on Bayesian statistics and the brain:

"The idea was born in 1983, when Geoffrey Hinton of the University of Toronto in Canada and Terry Sejnowski, then at Johns Hopkins University in Baltimore, Maryland, suggested that the brain could be seen as a machine that makes decisions based on the uncertainties of the outside world. In the 1990s, other researchers proposed that the brain represents knowledge of the world in terms of probabilities. Instead of estimating the distance to an object as a number, for instance, the brain would treat it as a range of possible values, some more likely than others.

A crucial element of the approach is that the probabilities are based on experience, but they change when relevant new information, such as visual information about the object’s location, becomes available. “The brain is an inferential agent, optimising its models of what’s going on at this moment and in the future,” says Friston. In other words, the brain runs on Bayesian probability. Named after the 18th-century mathematician Thomas Bayes, this is a systematic way of calculating how the likelihood of an event changes as new information comes to light (see New Scientist, 10 May, p 44, for more on Bayesian theory)."

link

(via Mind Hacks, Reverendbayes's Weblog)