Connectionist Learning - Some New Ideas

[Richard F Long]

	There may be another reason for the brain to construct
networks that are 'minimal' having to do with Chaitin and
Kolmogorov computational complexity.  If a minimal network corresponds
to a 'minimal algorithm' for implementing a particular computation, then
that particular network must utilize all of the symmetries and
regularities contained in the problem, or else these symmetries could be
used to reduce the network further.  Chaitin has shown that no algorithm
for finding this minimal algorithm in the general case is possible.
However, if an evolutionary programming method is used in which the
fitness function is both 'solves the problem' and 'smallest size' (i.e.
Occam's razor), then it is possible that the symmetries and regularities
in the problem would be extracted as smaller and smaller networks are
found.  I would argue that such networks would compute the solution less
by rote or brute force, and more from a deep understanding of the problem.
I would like to hear anyone else's thoughts on this.

Richard Long
rfl77551 at pegasus.cc.ucf.edu

General Research and Device Corp.
Oviedo, FL
& University of Central Florida