Xref: utzoo comp.ai.philosophy:772 comp.ai.neural-nets:3101
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From: ssingh@watserv1.waterloo.edu (Sneaky Sanj ;-)
Subject: Re: Continuous vs. discrete
Message-ID: <1991Mar26.220005.29122@watserv1.waterloo.edu>
Organization: University of Waterloo
References: <91082.223501DOCTORJ@SLACVM.SLAC.STANFORD.EDU> <1991Mar25.141743.21124@news.larc.nasa.gov> <MAGI.91Mar26022853@polaris.utu.fi>
Date: Tue, 26 Mar 1991 22:00:05 GMT
Lines: 76

Here's something  that was posted a while back on this subject.

From ssingh Sun Feb 10 22:33:54 EST 1991
Article 1750 of comp.ai.neural-nets:
Newsgroups: comp.ai.neural-nets
Path: watserv1!ssingh
>From: ssingh@watserv1.waterloo.edu (The Sanj-Machine aka Ice)
Subject: continuous vs discrete values for weights
Message-ID: <1991Feb2.001242.3473@watserv1.waterloo.edu>
Organization: University of Waterloo
Date: Sat, 2 Feb 91 00:12:42 GMT
Lines: 18

Could someone tell me if there is any significant difference regarding the
properties of neural networks with a finite set of states for connection
strengths as opposed to continuous values. Which is more biologically
accurate?

I always thought that neurons assume one of a finite set of strengths. It
is just that it is a very large set, so from our vantage point it
appears continuous. I would like to explore the dynamical properties of
nonlinear neural networks, so this is important.

Thanks in advance for your time.


-- 
"No one had the guts... until now!"  
$anjay $ingh     Fire & "Ice"     ssingh@watserv1.[u]waterloo.{edu|cdn}/[ca]
ROBOTRON Hi-Score: 20 Million Points | A new level of (in)human throughput...
"The human race is inefficient and therefore must be destroyed."-Eugene Jarvis


From utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!spool.mu.edu!uwm.edu!rpi!ispd-newsserver!kodak!isctsse!gabber!rao Sun Feb 10 22:34:08 EST 1991
Article 1770 of comp.ai.neural-nets:
Path: watserv1!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!spool.mu.edu!uwm.edu!rpi!ispd-newsserver!kodak!isctsse!gabber!rao
>From: rao@gabber.kodak.com (Arun Rao)
Newsgroups: comp.ai.neural-nets
Subject: Re: continuous vs discrete values for weights
Message-ID: <1991Feb5.165813.10038@usenet@kadsma>
Date: 5 Feb 91 16:58:13 GMT
References: <1991Feb2.001242.3473@watserv1.waterloo.edu>
Sender: usenet@usenet@kadsma (News Administrator)
Reply-To: rao@gabber.kodak.com (Arun Rao)
Organization: Image Electronics Center, Eastman Kodak Company
Lines: 22

In article <1991Feb2.001242.3473@watserv1.waterloo.edu>, ssingh@watserv1.waterloo.edu (The Sanj-Machine aka Ice) writes:
... [stuff deleted ]
|> 
|> I always thought that neurons assume one of a finite set of strengths. It
|> is just that it is a very large set, so from our vantage point it
|> appears continuous.
... [stuff deleted ]

	How large is very large ?  It appears unlikely to me that neuron
	activation could possess as much resolution as (say) even a typical
	binary float representation.  I don't remember having seen any numbers,
	but I would tend to think that if you need more than 8 bits of
	resolution to get a neural computational model to work, the biological
	plausibility of such a model is suspect.

	This is not to say, of course, that biological plausibility should be
	the acid test in evaluating models, especially application-oriented work.

	I'd be glad to hear about any experimental evidence that supports a
	considerably higher resolution in individual neuron activation.

	-Arun


-- 
"No one had the guts... until now!"  
$anjay $ingh     Fire & "Ice"     ssingh@watserv1.[u]waterloo.{edu|cdn}/[ca]
ROBOTRON Hi-Score: 20 Million Points | A new level of (in)human throughput...
!blade_runner!terminator!terminator_II_judgement_day!watmath!watserv1!ssingh!