Path: utzoo!utgpu!water!watmath!clyde!att!osu-cis!tut.cis.ohio-state.edu!mailrus!ames!haven!mimsy!oddjob!tank!uxc!ksuvax1!cseg!lag
From: lag@cseg.uucp (L. Adrian Griffis)
Newsgroups: comp.ai.neural-nets
Subject: Re: temporal domain in vision
Keywords: multiplex filter model
Message-ID: <724@cseg.uucp>
Date: 21 Sep 88 20:50:53 GMT
References: <233@uceng.UC.EDU>
Organization: College of Engineering, University of Arkansas, Fayetteville
Lines: 29

In article <233@uceng.UC.EDU>, dmocsny@uceng.UC.EDU (daniel mocsny) writes:
> In Science News, vol. 134, July 23, 1988, C. Vaughan reports on the
> work of B. Richmond of NIMH and L. Optican of the National Eye
> Institute on their multiplex filter model for encoding data on
> neural spike trains. The article implies that real neurons multiplex
> lots of data onto their spike trains, much more than the simple
> analog voltage in most neurocomputer models. I have not seen
> Richmond and Optican's papers and the Science News article was
> sufficiently watered down to be somewhat baffling. Has anyone
> seen the details of this work, and might it lead to a method to
> significantly increase the processing power of an artificial neural
> network?

My understanding is that neurons in the eye depart from a number of
general rules that neurons seem to follow elsewhere in the nervous system.
One such departure is that sections of a neuron can fire independent
of other sections.  This allows the eye to behave as though is has a great
many logical neuron without having to use the the space that the same number
of discrete cellular metabolic systems would require.  I'm not an expert
in this field, but this suggests to me that many of the special tricks
that neurons of the eye employ may be attempts to overcome space limitations
rather than to make other processing schemes possible.  Whether or not
this affects the applicability of such tricks to artificial neural networks
is another matter.  After all, artificial neural networks have space 
limitations of their own.

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