Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!ames!eos!riacs!danforth
From: danforth@riacs.edu (Douglas G. Danforth)
Newsgroups: comp.ai.neural-nets
Subject: Re: Step Function
Message-ID: <1683@hydra.riacs.edu>
Date: 9 Sep 89 02:38:04 GMT
Reply-To: danforth@hydra.riacs.edu.UUCP (Douglas G. Danforth)
Organization: Research Institute for Advanced Computer Science
Lines: 27
Keywords:memory,learning,generalization

Luke Leighton writes:
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Reply-To: zmacv61@doc.ic.ac.uk (L K C Leighton)
Organization: Imperial College Department of Computing

....  i saw memory recall-type neural nets as limited, as they have no means
to link one neural pattern to another (thought processing we do all the time).
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  Please take a look at Pentti Kanerva's book, "Sparse Distributed Memory"
1988, Cambridge MA, MIT Press.

  The fundamental place from which  Pentti begins is precisely the ability of
memory to link one pattern to another.   Input to memory triggers a
reconstructed output which in turn can act as more input.  This linking
of input-output associations can form long "pointer chains" so that
sequences, such as musical sonatas, can be recalled.  The complexity of the
patterns can be very great (hundreds to thousand of bits) and still be 
manageable and learnable.

   The dividing line between an artificial neural net and a "memory" is a
fuzzy one.  They share many similarities.

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Doug Danforth
danforth@riacs.edu
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