Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!eecae!tank!uwvax!uwslh!lishka
From: lishka@uwslh.UUCP (Fish-Guts)
Newsgroups: comp.ai.neural-nets
Subject: Re: NN Question
Message-ID: <418@uwslh.UUCP>
Date: 6 Mar 89 19:22:00 GMT
References: <32125@gt-cmmsr.GATECH.EDU> <10624@pasteur.Berkeley.EDU>
Reply-To: lishka@uwslh.UUCP (Fish-Guts)
Distribution: usa
Organization: U of Wisconsin-Madison, State Hygiene Lab
Lines: 103

In article <10624@pasteur.Berkeley.EDU> brp@sim.UUCP (bruce raoul parnas) writes:
>In article <32125@gt-cmmsr.GATECH.EDU> kirlik@hms3.gatech.edu (Alex Kirlik) writes:
>>Why should a net with only a few dozen neural units be
>>successful at mimicking human behavior that is presumably
>>the result of the activation of a tremendous number of
>>neurons?  That is, why should a small number of units
>
>I beg to differ substantially on this claim.  No man made neural networks have
>yet come close to modelling/mimicking human behavior, no matter what the level
>of abstraction we assume.  They do not reflect the temporal properties, and are
>totally incapable of *MANY* of the things humans can do.  Neural nets take 
>inputs and associate them with outputs, nothing more.  They do not reflect even
>the simplest levels of cognition!

     This all depends on what you claim is "human behavior."  Below is
quote taken from a paper in which the authors describe a neural
network that they use to model the pyriform (olfactory) cortex.   The
neural network contained about 300 artificial neurons, whereas the
piriform cortex of a rat contains about 10^6 neurons.  In the paper,
they show that their model does reproduce certain key characteristics
of piriform cortex (which is also found in humans, but is usually
studied in animals).  Presumably, this "behavior" of piriform cortex
also occurs in humans.  They have modeled this on a relatively coarse
level. 

     Granted, this may not be what most consider "human behavior" as
we all see it, but it is behavior of the human brain (IMHO).  Although
I think models of this sort are rare at this point in time, I would
expect that more will appear in the future.

>>I know that the validity of this question depends upon the
>>"level" at which we interpret our models, but, after all,
>
>At no level is this valid, i believe.

     As a student of AI, with a couple semesters of neurobiology under
my belt, I disagree.  At certain "lower" levels there have been been
some interesting neural nets that model certain low-level behaviors in
animals.  

     As a practical example, I offer this quote from the abstract of a
paper by Matthew A. Wilson and James M. Bower titled "A Computer
Simulation of Olfactory Cortex with Functional Implications for
Storage and Retrieval of Olfactory Information." The authors were
*neurobiology* graduate students of one of my professors, Lewis B.
Haberly. 

	Based on anatomical and physiological data, we have 
	devloped a computer simulation of piriform (olfactory)
	cortex which is capable of reproducing spatial and
	temporal patterns of actual cortical activity under a 
	variety of conditions. [...]  We have shown that 
	different representations can be stored with minimal
	interference, and that following learning these
	representations are resistant to input degradation,
	allowing reconstruction of a representation following
	only a partial presentation of an original training
	stimulus.  Further, we have demonstrated that the
	degree of overlap of cortical representations for
	different stimuli can also be modulated.  For instance
	similar input patterns can be induced to generate
	distinct cortical representations (discrimination),
	while dissimilar inputs can be induced to generate
	overlapping representations (accomodation).  Both
	features are presumably important in classifying
	olfactory stimuli.
	
This quote is reproduced without permission.  At the time the paper
was written, the authors could be reached at the Computation and
Neural Systems Program, Division of Biology, California Institute of
Technology, Pasadena, CA 91125

>I think that a great many people view neural networks as good models for what
>goes on inside our heads.  Since these models are, mainly, discrete time
>automata they do not reflect the fact that real neural systems are, 
>essentially,nonlinear continuous-time multi-dimensional vector spaces 
>in which the neurons
>evolve in time.  So while they are real neat computational tools, they are far
>from representing real neural processes.

     I disagree; I feel that the above paper proves my point.  One
interesting point, however, is that the neural network used in the
above model used artificial neurons that modeled behavior of
individual neurons in the piriform cortex, complete with
considerations of membrane potential, delay due to the velocity of the
signal through the axon, and time course, amplitude, and waveform due
to particular ionic channel types (of which Na+, Cl-, and K+ channels
types were included in the model).  In other words, the model was
*NOT* a simple neural network based on simple "units" or
McCulloch-Pitts neurons.  However, it *was* a neural network, although
its artificial neurons were more complex than most used today.

>bruce (brp@sim)

				.oO Chris Oo.
-- 
Christopher Lishka                 ...!{rutgers|ucbvax|...}!uwvax!uwslh!lishka
Wisconsin State Lab of Hygiene                   lishka%uwslh.uucp@cs.wisc.edu
Immunology Section  (608)262-1617                            lishka@uwslh.uucp

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