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Some already exist specifically for neural data analysis and simulation, such as PyMVPA2 and Brian3 respectively.

"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.

Miller and Cohen propose an Integrative Theory of Prefrontal Cortex Function. The two theorize that “cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represents goals and means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task” (Miller & Cohen, 2001). Essentially the two theorize that the prefrontal cortex guides the inputs and connections which allows for cognitive control of our actions.

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).

In 2007, Jung and Richard Haier, now professor emeritus of psychology at the University of California, Irvine, developed the first comprehensive theory drawn from neuroimaging of how the brain gives rise to intelligence.

Applying existing theories of how information flows in the brain, Jung and Haier hypothesized that neural signals travel from nodes near the back of the brain, where sensory data is collected and synthesized, to those in the frontal lobes, which are responsible for decision making and planning. The connections between these nodes, they argued, are just as critical as the nodes themselves.