Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!tut.cis.ohio-state.edu!cs.utexas.edu!uunet!portal!cup.portal.com!dan-hankins
From: dan-hankins@cup.portal.com (Daniel B Hankins)
Newsgroups: comp.ai
Subject: Re: Symbolic Connectionism? (was Re: What IS a symbol?)
Message-ID: <17877@cup.portal.com>
Date: 3 May 89 06:01:39 GMT
References: <11313@bcsaic.UUCP> <17467@cup.portal.com> <5418@cs.Buffalo.EDU> <17548@cup.portal.com> <15323@gryphon.COM>
Organization: The Portal System (TM)
Lines: 69

In article <15323@gryphon.COM> sarima@gryphon.COM (Stan Friesen) writes:

>In article <17548@cup.portal.com> dan-hankins@cup.portal.com (Daniel B
>Hankins) writes:
>
>>     I'll grant that *most* ANN systems in use are fixed-topology, where
>>the topology is designed in advance.  I personally don't find them very
>>interesting - I'm into generality.
>
>     Yes, generality is nice, but I know of no natural neural-nets that
>have generality at a low level.  The human brain only achieves generality
>by consisting of numerous complex subnets that are individually quite
>specialized. 

     I imagine that you are thinking of perceptrons (Did I spell that
right?), and other recent findings in neuroscience.

     You may well be right.  But I think that the functions that are
pre-programmed are likely the ones that are most closely tied to the
sensory organs and self-regulating systems (such as the heart and lungs).

     In any case, we don't need to understand these organizations in great
detail.  Nature didn't design the subnet structures; they evolved. 
Likewise, we can use GAs to evolve the subnets, later integrating them into
a more coherent whole.

     I understand that there is a lot of pre-built structure in places like
the optical cortex, the auditory center and so on.  Is there a lot of this
kind of specialization in the cerebrum?  Since its function appears to be
to integrate the functioning of the other areas and produce meaningful
action (i.e. it thinks), I would hazard a guess that it shows less of this
kind of specialized topology than the other areas.  I do have a foggy
recollection of a finding that the neurons in that area were arranged in
clumps, and the clumps in clumps and so on, but I didn't hear anything to
indicate that there was any specific ordering to the clumping.


>>     When I write of connectionist systems that can achieve intelligence
>>(or combinations of connectionist and symbolic), I am thinking of more
>>biologically accurate approaches - non-back-propagation, self-organizing
>>topology, and unsupervised learning.
>
>     Hold on! What's wrong with back propagation!  Every biological neural
>system more complex than a reflex loop has extensive back-propagation! It
>is a key element of feed-back based control in such systems.  I do not
>believe that any adaptive system can be achieved without it.

     I think I may not have made myself clear.  When I said non-back-prop,
I meant systems that do not conform to the standard backprop systems in use
in the vast majority of applications.  In any case, back-propagation in the
sense it is used in these nets is a terrible
oversimplification.

     My latest information on synaptic reinforcement indicates that
postsynaptic neuron firing does not, by itself, cause synaptic
reinforcement.  Rather, the postsynaptic neuron must fire _quite soon_
after the presynaptic neuron does in order to cause reinforcement.  Or
perhaps it is the other way around.  In any case, firing of pre- and
post-synaptic neurons must be proximate in time in order to cause synapse
reinforcement.

     And of course, even that is only a part of the story.  Some evidence
suggests that neurotransmitters are enhanced exponentially rather than
linearly.  And how many ANN systems take into account the local suppression
effect, where the firing of a neuron inhibits the firing of neighboring
neurons briefly, avoiding destructive synchronization?


Dan Hankins