New Ideas for Brain Modelling 7
  This paper updates the cognitive model, firstly by creating two systems and
then unifying them over the same structure. It represents information at the
semantic level only, where labelled patterns are aggregated into a
'type-set-match' form. It is described that the aggregations can be used to
match across regions with potentially different functionality and therefore
give the structure a required amount of flexibility. The theory is that if the
model stores information which can be transposed in consistent ways, then that
will result in knowledge and some level of intelligence. As part of the design,
patterns have to become distinct and that is realised by unique paths through
shared aggregated structures. An ensemble-hierarchy relation also helps to
define uniqueness through local feedback that may even be an action potential.
The earlier models are still consistent in terms of their proposed
functionality, but some of the architecture boundaries have been moved to match
them up more closely. After pattern optimisation and tree-like aggregations,
the two main models differ only in their upper, more intelligent level. One
provides a propositional logic for mutually inclusive or exclusive pattern
groups and sequences, while the other provides a behaviour script that is
constructed from node types. It can be seen that these two views are
complimentary and would allow some control over behaviours, as well as
memories, that might get selected.
