Dynamically Reconfigurable Universal Learning Neurocomputer

In this lecture by Victor Eliashberg, I was introduced to the idea of the brain as a universal learning computer. The lecture itself, having not been familiar with any of the material covered, I found quite confusing, with seemingly quite a few disparate elements 'thrown together'. Having conducted a little further reading into the topic, however, it became quite clear what the fundamental thesis of the work was.

The underlying motivation is the assertion that in order to understand the brain, one must study it as a whole, and not decompose it into functional regions for relatively independent study (the major approach used in cognitive psychology). The reason for this decomposition in most studies on the brain is that it is practically impossible to study the human brain in its entirety when fully developed – and it is this state where it exhibits behaviours which are of interest to psychologists etc. Given this, it would then seem reasonable to study the brain in its 'starting state', i.e. the state in which learning has not yet begun.

Given that 'W' represents the world, 'B' represents the brain, and 'D' represents the body (of the human), such that a system (W, D, B) is one which we wish to describe (in other words the behaviour of the human in the world), and that B(t) is the formal representation of B at time t (such that B(0) is the brain in its starting state), then four propositions are made:

(1) It is possible to find a relatively small representation of B(0). It is speculated that this amount of information may fit on a single floppy disk (?!).
(2) Any formal representation of B(t) when t is large (on the scale of years perhaps) would be huge (possibly terabytes). This is due to the presence of the persons personal experiences.
(3) It is practically impossible to reverse engineer B(t) (when t is large) without first reverse engineering B(0).
(4) It is practically impossible to model the behaviour of the system (W, D, B) without a representation of B(t).

Based on these four propositions, the project (known as Brain 0) is to reverse engineer the starting state of the brain. Eliashberg provides a possible model: that of a universal learning computer, based on a universal Turing machine.