Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!tut.cis.ohio-state.edu!ucbvax!pasteur!sim!brp
From: brp@sim.uucp (bruce raoul parnas)
Newsgroups: comp.ai.neural-nets
Subject: Re: NN Question
Message-ID: <11120@pasteur.Berkeley.EDU>
Date: 15 Mar 89 17:51:14 GMT
References: <32125@gt-cmmsr.GATECH.EDU> <10624@pasteur.Berkeley.EDU> <418@uwslh.UUCP>
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Reply-To: brp@sim.UUCP (bruce raoul parnas)
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In article <418@uwslh.UUCP> lishka@uwslh.UUCP (Fish-Guts) writes:
>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

By "behavior" i refer to the underlying strategy, if you will, governing the
actions, not simply the actions themselves.  Given a set of inputs and a set
of outputs it is quite easy to construct, for example, a simple digital
circuit made from combinational logic which can perform the required tasks, 
yet no one would argue that this, in any way, represents the brain.  Cognition
is something we do not yet understand and we can do little more than model
the responses rather than the process.  A small child can repeat words that
he/she can not understand; is this an understanding of the language?

!>>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.  
I think we're interpreting the word "level" in the original posting 
differently.  I believed it referred to levels of interpretation of a cognitive
model as opposed to modeling of lower-level functions.  i do agree that some of
these latter functions are quite well understood and have been modeled well.
Prime examples of this are the mechanisms in the sensory periphery (see, for
example, Feld, et al in Advances in Neural Information Processing Systems due
around April).  I think that models of cognition, however, are not very useful
at any level toward an understanding of the "big picture" yet, although i hope
that further work will change this.

!     As a practical example, I offer this quote from the abstract of a

[quote concerning modeling of the olfactory system]

the paper you reference (removed for brevity) is quite interesting.  i still
feel that it models the results rather than the cause of the behavior, but it
is, i believe, a step in the right direction.  the inclusion of the temporal
aspect of neurons is crucial to a realistic model.

!>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.

I misspoke.  what i meant to say was that neural networks are from modeling
COGNITIVE neural processes such as memory and the like.  the peripheral
sensory system, including olfaction, is quite a bit easier to model (as
mentioned above), and the quote you reproduced corroborates this.  i have no
arguement against these models, only those of higer cortical function.
!>bruce (brp@sim)
!
!				.oO Chris Oo.