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The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation.

Friston recently expanded on this to suggest an inversion method for hierarchical Bayesian dynamic models and to point out that the brain, in principle, has the infrastructure needed to invert hierarchical dynamic models [6].

In a recent review, Hegde and Felleman pointed out that the “Bayesian framework is not yet a neural model. [The Bayesian] framework currently helps explain the computations that underlie various brain functions, but not how the brain implements these computations” [2]. This paper is an attempt to fill this gap by deriving a computational model for cortical circuits based on the mathematics of Bayesian belief propagation in the context of a particular Bayesian framework called Hierarchical Temporal Memory (HTM).

"Scientists are now able to switch the focus from particular regions of the brain to the connections between those regions," says Sherif Karama, a psychiatrist and a neuroscientist at McGill University's Montreal Neurological Institute.

A quantifiable "general intelligence factor," known as g, can be statistically extracted from scores on a battery of intelligence tests.

In 2001, Thompson showed that it is correlated with volume in the frontal cortex, a result consistent with a number of studies that have linked intelligence to overall brain size.

Over the past decade, neuroscientists have found that real brains seem to work in this way. In perception and learning experiments, for example, people tend to make estimates - of the location or speed of a moving object, say - in a way that fits with Bayesian probability theory. There's also evidence that the brain makes internal predictions and updates them in a Bayesian manner. When you listen to someone talking, for example, your brain isn't simply receiving information, it also predicts what it expects to hear and constantly revises its predictions based on what information comes next. These predictions strongly influence what you actually hear, allowing you, for instance, to make sense of distorted or partially obscured speech.

In fact, making predictions and re-evaluating them seems to be a universal feature of the brain. At all times your brain is weighing its inputs and comparing them with internal predictions in order to make sense of the world.

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.

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.

The number of neurons and synapses in the simulation exceed those in a cat’s brain;