Composite networks

[STAY8026%IRUCCVAX.UCC.IE@bitnet.cc.cmu.edu]

Hi,
 
I am interested in modelling tasks in which invariant information from
previous input-output pairs is brought to bear on the acquisition of current
input-output pairs.  Thus I want to use previously extracted regularity to
influence current processing.  Does anyone think this is feasible??
 
At year's memory conference in Lancaster (England) Dave Rumelhart mentioned
the need to develop nets which incorporate a distinction between learning
and memory and exploit the attributes of both.   Thus,  our present learning
procedures, such as BACKPROP, are useful for combining, without interference,
multiple input-output pairs, while competitive learning systems are useful
for computing regularities.  How about attempting to combine the two? Has
anyone tried?
 
My suspicion is that such composite networks might be usefully applied to
a number of issues in natural language processing, such as, perhaps, the
syntactic embeddings considered by Pollack (1990, Artificial Intelligence).
At first I thought that a sequential net of the sort discussed by
Hinton and Shallice (1991 Psych. Rev.) might fit the bill, but now I'm not
sure.
 
Any ideas or suggestions?
 
P. J. Hampson
University College Cork
Ireland