Researchers in computational neuroscience want to come up with a single theory to explain how the brain works—Bayesian statistics may provide the answer. An article in NewScientist asks: Is this a unified theory of the brain? (although a subscription to NewScientist is required to access the article, the Mind Hacks blog found a link to a copy of the article posted elsewhere).
Neuroscientist Karl Friston and his colleagues have proposed a mathematical law that some are claiming is the nearest thing yet to a grand unified theory of the brain. From this single law, Friston’s group claims to be able to explain almost everything about our grey matter. [...]
Friston’s ideas build on an existing theory known as the “Bayesian brain”, which conceptualises the brain as a probability machine that constantly makes predictions about the world and then updates them based on what it senses.
The article goes on to explain the Bayesian brain and how it is a group of related approaches that use Bayesian probability theory to understand different aspects of brain function. What Friston has done is introduce the framework for a “unifying theory”—a theory that ties everything together—using the idea of a prediction error (to minimize surprise) as “free energy”. Friston describes the theory as follows:
In short, everything that can change in the brain will change to suppress prediction errors, from the firing of neurons to the wiring between them, and from the movements of our eyes to the choices we make in daily life.
Many researches aren’t yet convinced that the theory will be unifying—although they aren’t denying the possibility—and concerns have been raised that the theory may not be testable or be used to build machines that mimic the brain. But experiments are being proposed to help advance and prove the theory, and many agree that it has tremendous potential.