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From: gary@desi.ucsd.edu (Gary Cottrell)
Newsgroups: comp.ai.neural-nets
Subject: Re: neural holography !?
Message-ID: <7649@sdcsvax.UCSD.Edu>
Date: 1 Jan 90 23:40:55 GMT
References: <1904@tellab5.TELLABS.COM>
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Reply-To: gary@desi.UUCP (Gary Cottrell)
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In article <1904@tellab5.TELLABS.COM> neuron@tellab5.TELLABS.COM (Don Graft) writes:
>I was wondering if anybody is aware of any work done on holographic paradigms
>in neural nets.  Of course I am aware of work done in the late 60s and early
>70s by such people as Westlake, Gabor, and Pribram, and of recent work
>implementing *optic* systems for heteroassociative recall and for (this
>amuses me) interconnecting traditional connectionist nets.  However, noone
>seems ever to have actually simulated neural holographic heteroassociative
>memory and, as far as I can tell, nobody is even trying.  Given the well-known
...
>
>Donald Graft      ...uunet!tellab5!neuron

Janet Metcalfe has been modeling the hippocampal memory system as a
discrete holographic system since 1981. The CHARM (Composite
Holographic Associative Recall and Recognition Memory) model works by
convolving pairs to be associated and then adding them into a memory
trace.  A single item is stored by convolving it with itself.
Retrieval is done by correlation. CHARM accounts for a wide range of
psychological data and successfully predicts new data. She has two
Psych Review papers on it under the name Metcalfe Eich, in 1982 vol 89
p627-661, and 1985 Vol 92, p1-38, and a new paper on blended memories
coming out in JEP General shortly.

She had a poster at the recent NIPS conference that shows how her
model accounts for "the function" in the recognition failure paradigm.
The data in the recognition failure paradigm is so orderly that
Tulving has called it a psychological law. "The function" (imaginative
name!) is a function relating the probability that a test item in a
paired associate memory task will be recognized given that it was
recalled. You might imagine that if you recalled an item from a list,
then you would recognize it, but this is not the case! There is only a
slight dependence, and no mechanistic memory model has accounted for
it except hers.

I don't know if she has preprints to give out, but you can reach her at
metcalfe@cogsci.ucsd.edu.

The model is very interesting because storing an association is very
fast.  I think it has very good complementary properties to some of
the abilities of a back prop learning system.

gary cottrell 619-534-6640 Sec'y: 619-534-5288 FAX: 619-534-7029
Computer Science and Engineering C-014
UCSD, 
La Jolla, Ca. 92093
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