  The paper tackles four basic questions associated with human brain as a
learning system. How can the brain learn to (1) mentally simulate different
external memory aids, (2) perform, in principle, any mental computations using
imaginary memory aids, (3) recall the real sensory and motor events and
synthesize a combinatorial number of imaginary events, (4) dynamically change
its mental set to match a combinatorial number of contexts? We propose a
uniform answer to (1)-(4) based on the general postulate that the human
neocortex processes symbolic information in a "nonclassical" way. Instead of
manipulating symbols in a read/write memory, as the classical symbolic systems
do, it manipulates the states of dynamical memory representing different
temporary attributes of immovable symbolic structures stored in a long-term
memory. The approach is formalized as the concept of E-machine. Intuitively, an
E-machine is a system that deals mainly with characteristic functions
representing subsets of memory pointers rather than the pointers themselves.
This nonclassical symbolic paradigm is Turing universal, and, unlike the
classical one, is efficiently implementable in homogeneous neural networks with
temporal modulation topologically resembling that of the neocortex.
