Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!uflorida!haven!aplcen!jhunix!ins_atge
From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards)
Newsgroups: comp.ai.neural-nets
Subject: Re: NN Question (how can a few neurons mimic the brain?)
Summary: NN's and neural plasticity
Keywords: NN's and brains
Message-ID: <1022@jhunix.HCF.JHU.EDU>
Date: 7 Mar 89 01:00:06 GMT
References: <32125@gt-cmmsr.GATECH.EDU> <11945@swan.ulowell.edu>
Reply-To: ins_atge@jhunix.UUCP (Thomas G Edwards)
Distribution: usa
Organization: The Johns Hopkins University - HCF
Lines: 58

In article <11945@swan.ulowell.edu> sbrunnoc@hawk.ulowell.edu (Sean Brunnock) writes:

>  I find that there are some people who are under the impression
>that by linking together many specialized programs(a vision 
>processor, a language processor,...), they will be able to create 
>something akin to the human mind. I do not subscribe to this
>theory because the human brain is pretty much uniform. This 
>fact becomes dramatically obvious in the cases of people who have
>had accidents resulting in the damage of sections of the brain.
>If the damaged section performed a specialized function, then
>for awhile, the person will not be able to perform that action.
>After some time, the rest of the brain is able to assimilate
>the functions performed by the damaged section and the person
>is able to function normally again. 

   It is indeed correct that the brain is capable of changing the
functions of some of its different parts to a limited extent
(the classical example is loss of a nerve going to the skin of the
hand, and neurons which originally were connected strongly to the
part of the skin served by that nerve connect themselvs to nerves
going to other parts of the hand).
   However, the brain -does- have a great deal of differentiation.
(just look at cerebellum vs brain stem vs cereberal cortex).
In addition, large enough damage does produce irreperable dammage
(such as damage to Broca's area involves in speech propduction
leading to Broca's aphasia).
   Moreover, after learning, neurons "differentiate" across the
network.  Look at the hidden units of a feedforward backpropogated
NN.  Each hidden unit will tend to code for a certain part of
the input signal.  If we excise a neuron or two, we typically have
enough distributed representation for the NN to still work.  If
we excise more, we have to re-teach the network.  Eventually, if we
excise enough neurons, the network will not be able to work at all
(with size depending on the complexity of the problem, which is also
closely related to the number of patters to be coded for and size of
input field).  There is, by the way, a whole science to figuring out
how many hidden units to excise from a network to maintain the 
minimum number of neurons and still have the NN operate properly.
   (I personally have a gut feeling that genetic algorithms will help
NN researchers "evolve" alot of NN structure, in a similar way to what
happened to humans).>
>  I look at the market and current research and I see a lot of
>neural network expert systems, handwriting recognizers, and
>image processors. The term neural network here is very misleading.
>I believe that a neural network should be able to learn to do 
>anything and still remain flexible enough to deal with abrubt 
>changes as the human brain is capable of doing. 
      Ah, it all depends on the learning algorithm.  Infact, it may be
that there are meta-learning rules in brain (i.e. a network which
is taught using neron-level learning rules to "learn" on a larger scale,
including input selectivity, some ammount of theorem proving, and
alot of other "symbolic AI" stuff that people think NN's will replace,
albeit on a massively-parallel fault-tolerant scale).
-Thomas Edwards
ins_age@jhuvms (BITNET)
tedwards@nrl-cmf.arpa

#include<disclaimer.hs>   /* ported to connection machine */