🚀 go-pugleaf

In article <2121@peora.UUCP> jer@peora.UUCP (J. Eric Roskos) writes:
>if you think of it in terms of a table, table entries tend to "attract"
>nearby guesses, so that from an approximation you get pulled into the
>memorized answer. (Likewise, if you make an initial guess that is nearer
>to another (wrong) answer, you may get pulled to that one instead and have
>trouble finding the right answer as a result.) Very simple published
>algorithms (albeit slow ones on a sequential machine) exist for modelling
>simple forms of this operation, although other research has suggested that
>a variety of specialized "functional units" exist in the brain which
>aren't covered by that model. (Incidentally, some very interesting
>research in cognitive psychology shows that some classes of problem
>solving can be modeled in terms of n-dimensional spaces, and you can even
>produce surprisingly unexpected artifacts of this spatiality -- for
>example, people categorizing things using attributes of the objects that
>are highly nonobvious, seemingly based entirely on this spatial distance
>-- which of course really isn't spatial per se., probably, but is probably
>an artifact of the number of partitions of the "bits" present which
>are used to store the data.)

For more on this subject, I would recommend _Parallel Models of
Associative Memory_ by Geoffrey Hinton and James Anderson.  Some of
the chapters include:

	"Models of Information Processing in the Brain"
	"A Connectionist Model of Visual Memory"
		by J>A> Feldman
	"Holography, Associative Memory, and Inductive Generalization"
		by David Willshaw
	"Implementing Semantic Networks in Parallel Hardware"
	"Catagorization and Selective Neurons"
		by James Anderson and Michael Mozer

	The book is published by Lawrence Erlbaum Associates(1981),
and is available in most moderately disreputable bookstores.

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