  This paper focuses on how bottom-up neocortical models can be developed into
eigenfunction expansions of probability distributions appropriate to describe
short-term memory in the context of scalp EEG. The mathematics of
eigenfunctions are similar to the top-down eigenfunctions developed by Nunez,
albeit they have different physical manifestations. The bottom-up
eigenfunctions are at the local mesocolumnar scale, whereas the top-down
eigenfunctions are at the global regional scale. However, as described in
several joint papers, our approaches have regions of substantial overlap, and
future studies may expand top-down eigenfunctions into the bottom-up
eigenfunctions, yielding a model of scalp EEG that is ultimately expressed in
terms of columnar states of neocortical processing of attention and short-term
memory.
