Question:
I was wondering if anyone at Argonne was studying the brain or
learning processes in which chaos (or tortuosity of state-space) plays a role
in the "trial" part of trial-and-error learning. I have thought that maybe
the state-space of activity in the brain might tend to be more tortuous (and
thus variable) in regions of state-space that are unfamiliar, or where
experience has not taken the state of the system yet. If experience is the
tracking of system state through this space, and if Hebbian associative
learning "convergences" experience paths through the space (making these paths
themselves into attractors), then learning might "dechaoticize" the areas
around these experienced paths, making them repeatable and reliable in
activity. So "trial" would only be necessary (and possible) in unfamiliar
circumstances, and learning would "iron out" the trial in favor of giving the
system the ability to converge on (or be attracted to) familiar states in
familiar situations. That might account for classical or perceptual
conditioning, but what about operant or behavioral conditioning? I think that
if a central reward signal was available to the system, that the amount of
association taking place should be proportional to reward. Then the system
will learn more and retain more function from favorable situations than
unfavorable ones.
Replies:
I am not at Argonne but. . . It is an interesting idea (if I
understand it correctly). In an abstract way I suppose that is how neural
networks are supposed to work - they go from a completely random state with
random (Chaotic) output upon specified inputs to more specific output once
learning has taken place. I think I have seen this described as energy minima
in the neural net and because of the present sophistication of neural nets
that is most like classical conditioning. In behavioral conditioning the
brain learns by reward or punishment to a previous response. Some people are
looking at models that incorporate state dependent responses (that are
contingent on a previous stimuli as well as the present one). I have not seen
this used to let the net program itself (enhance learning). My guess is that
the reason for this is in how much more computational power is required to
adjust neural nets in their current form. As some of my colleagues have noted
this is one of the weaknesses of neural networks in their current form. The
brain does not appear to need to take up excessive amounts of processing
capacity in order to adjust its net to new learned information.
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