A Bayesian Approach to the Brain

The July Report on Progress, by Florent Meyniel, Ph.D., explores the Bayesian concept of the brain, a mathematical theory to neuroscience.

According to the article, Bayesian concepts are appealing to many researchers in fundamental and applied research, including neuroscience. Bayesian tools, part of probability theory, are useful whenever quantitative analysis is needed, such as in statistics, data mining, or forecasting. However, Bayesian concepts have much further reaching implications in neuroscience. They are essential to the way we think about the brain.

“Several aspects of Bayes’ rule are noteworthy. First, it is extremely general – H (hypothesis) and D (data) may be any sort of variables as long as they can be assigned a probability. Second, Bayes’ rule is quantitative: the posterior probability on the left hand side accepts only one value that depends on the terms in the right hand side. This means that Bayes’ rule offers a unique way to combine uncertain quantities such as current evidence and prior knowledge in order to estimate the likelihood of a conclusion. In that sense, Bayes’ rule is normative: any other estimate is an over- or under-estimation of the likelihood of the conclusion. This normative nature of Bayes’ rule can be seen as an extension of classical logic. With classical logic, one can derive the validity of a conclusion, which is either true or false from premises that are known for sure. With Bayes’ rule, one can derive the likelihood of a conclusion, which varies on a continuum, from premises that suffer from uncertainty. “

To read the entire article, click here.

– Blayne Jeffries

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