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Article 6407 of comp.ai.philosophy:
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>From: long@next3.acme.ucf.edu (Richard Long)
Newsgroups: comp.ai.philosophy
Subject: Re: Generalized Distributed Memory
Message-ID: <1992Jul1.140935.12225@cs.ucf.edu>
Date: 1 Jul 92 14:09:35 GMT
References: <mike.709873021@psych.ualberta.ca>
Sender: news@cs.ucf.edu (News system)
Organization: University of Central Florida
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In article <mike.709873021@psych.ualberta.ca> mike@psych.ualberta.ca (Mike  
Dawson) writes:
> long@next1.acme.ucf.edu (Richard Long) writes:
> 
> >In article <650@trwacs.fp.trw.com> erwin@trwacs.fp.trw.com (Harry  
Erwin)  
> >writes:
> >> Initial draft, distributed for comment.
> >(plausible holographic memory model for the cerebellum deleted)
> 
> >Your interpretation of cerebellar structure as an interference hologram  
is  
> >interesting to me, if only because the proposed mechanism can be used  
for  
> >other purposes.  However, holographic models in general suffer from a  
> >severe conceptual limitation; namely, THEY ARE LINEAR MODELS.

> Linearity vs. nonlinearity really isn't the issue here.  In a simple,
> linear distributed memory, after a pattern is learned one can retrieve
> it by presenting only part of it -- the system is, in general, a
> pattern completer.  The memory system doesn't need the whole (learned)
  ^^^^^^^^^^^^^^^^^
> pattern to be input in order to regenerate it.
> 
    Pattern completion is a property of attractors, of which linear  
systems can have at most one.  If you superimpose two such patterns onto  
your linear system, you get a single new attractor which is the linear  
combination of the two.  Both patterns will be retrieved, if any, by the  
presentation of a partial pattern, or any pattern!  
    That's true if what you have is a dynamical system--which a  
conventional hologram isn't.  In a hologram, I can retrieve one of many  
superimposed images by selecting a reference wave with a particular  
frequency and/or direction.  In any case, I cannot perform pattern  
completion.  Instead, I am referencing a particular whole image by a  
prespecified and unique signal, isomorphic to retrieving a computer's  
information by giving it the address.  I will make the strong claim that  
linear content-addressable memories are not possible (unless, of course, I  
simply use every possible sub-pattern as the unique address of 2^n copies  
of my original pattern :) ).  I'm sure some kind soul will correct me if  
I'm wrong ;^)
> --
> Michael R.W. Dawson                       email: mike@psych.ualberta.ca
> Biological Computation Project, Department of Psychology
> University of Alberta, Edmonton, AB CANADA T6G 2E9
> Tel:  +1 403 492 5175   Fax: +1 403 492 1768

--
Richard Long
Institute for Simulation and Training
University of Central Florida
12424 Research Parkway, Suite 300, Orlando, FL 32826
(407)658-5026, FAX: (407)658-5059
long@acme.ucf.edu


