Editing Memory-prediction framework (section)

=== Other terms ===
Hawkins has extensive training as an electrical engineer.  Another way to describe the theory (hinted at in his book) is as a [[Computational learning theory|learning]] [[hierarchy]] of [[Feed forward (control)|feed forward]] [[stochastic]] [[state machine]]s.  In this view, the brain is analyzed as an encoding problem, not too dissimilar from future-predicting error-correction codes.  The hierarchy is a hierarchy of [[abstraction]], with the higher level machines' states representing more abstract conditions or events, and these states predisposing lower-level machines to perform certain transitions.  The lower level machines model limited domains of experience, or control or interpret sensors or effectors.  The whole system actually controls the organism's behavior.  Since the state machine is "feed forward", the organism responds to future events predicted from past data.  Since it is hierarchical, the system exhibits behavioral flexibility, easily producing new sequences of behavior in response to new sensory data.  Since the system learns, the new behavior adapts to changing conditions.

That is, the evolutionary purpose of the brain is to predict the future, in admittedly limited ways, so as to change it.