From: mcochran@nyx.cs.du.edu (Mark A. Cochran)
Subject: Re: Myelin (Was Re: Spoken Like a True ProLifer)
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Date: Sat, 23 Jan 93 18:08:55 GMT

In article <JBATES.93Jan22140558@pinocchio.encore.com> jbates@encore.com (John W. Bates) writes:
>In article <1993Jan22.035802.8755@mnemosyne.cs.du.edu> mcochran@nyx.cs.du.edu (Mark A. Cochran) writes:
>
>>>Sorry, Mark, but you've got things a little mixed up. In networking
>>>lingo, hypercubes are a network designed for supercomputing, with
>>>a node at each vertex connecting to each neighboring vertex. It's not
>>>related to neural networks at all. The closest neural network design
>>>I can think of is James Anderson's "brain state in a box", in which 
>>>each output pattern is a vertex of an n-dimensional box. Nice model 
>>>for associative memory, but n tends to have to be very large (in 
>>>computational terms) for it to be useful.
>>>
>> It's a good thing I made sure to deny any real knowledge of
>> hyper-cubes then, isn't it? :)
>> I still bet you can't build one out of C=64's though.... :)
>
>Oh, you could build one, all right. It might even be more useful than
>a single C-64. That's not saying much. (Actually, on a side note, my
>father still uses the C-64 he bought in 1982. He really likes it, but
>soon might be upgrading to a 286. Whoooeee! (And besides, look at what
>Chaney accomplishes with his C-64...))
>
Ok, so you can build one, and use it to play simple little games, or
make yourself look foolish like Airplane Boy. Good argument for not
building one. ;)

>>>I've been leary of bringing models into this discussion, since it is
>>>often hard to relate models of neural networks to actual neural
>>>networks. But now, let me refer to a model by Stanislas Dehaene and
>>>Jean-Pierre Changeux, which simulated the performance of human
>>>infants in Piaget's A not B task. Their results approximated the
>>>performance of human infants. 
>>>
>>>The interesting part of the experiment, though, was that they varied
>>>the amount of "noise" that the network received. At high levels of
>>>noise, the network performed at the level of a 7-month old infant,
>>>but at low levels, it performed at the level of a 12-month old
>>>infant.  Noise levels seemed to correspond to the development of
>>>myelin in the frontal lobes.  (from the _Journal of Cognitive
>>>Neuroscience_ 1:3, S. Dehaene and J.P.  Changeux, A simple model of
>>>prefontal cortex function: delayed response tasks)
>>>
>> Interesting. If they were able to jump the noise level around to
>> approximate various developmental stages, I wonder if they could/did
>> jump it up to a level that would approximate that development of, say,
>> a 22 week fetus?
>> Be interesting to see the results if they did. It could shed some
>> light on this subject, at least.
>
>Well, the problem with models is that it is entirely possible to read
>too much into them. The strongest conclusion you can make from the data
>gathered is that the performance of the network matches the performance
>of a child at a specific age. Since the mechanism by which the network
>befores does not map one-to-one with the mechanism by which the child
>performs, it's still only an approximation. Extrapolating the performance
>of the network beyond the data we have from actual experimentation is
>really only guesswork.
>
>Of course, we can determine the amount of noise in an unmyelinated vs.
>a myelinated axon, by using the cable equations. We know that the
>performance of a network will degrade rapidly when the noise increases 
>and/or when the size decreases. But, without experimental data, we can't
>be sure that our simulated network matches the actual performance of the
>human mind.
>
I'm not crazy enough to think any solid answers would come from the
experiment, but a rough best-guess aproximation would be a starting
point for further work.

>> [Crabs and Squid deleted, since I've already had lunch]
>
>>>>>>> The resonable work in question, though, is thought. Just as you
>>>>>>> can't use a 4 bit 16K RAM computer as an effective file server, I
>>>>>>> don't see how you cna use the similarly limited abilities ofthe
>>>>>>> pre-myelinated neural system as a 'thought server'.
>>>
>>>>>>I'm reserving my judgment on the matter for a time when we know
>>>>>>more about all the issues involved.  The hypomyelinated mice
>>>>>>discussed later in the post may be our best window into this issue.
>>>>>>In the meantime, it must be obvious to everyone reading this thread
>>>>>>(all 3 of us :-) that neither of us has any clue about whether
>>>>>>myelin is necessary or not.  I think that there is at least a fair
>>>>>>amount of information on the biological side that suggests that
>>>>>>myelin is not as central as some claim.  On the other hand, your
>>>>>>arguments about the presumed complexity of the network are
>>>>>>certainly thought-provoking (there's that smell again...myelin
>>>>>>burning or something).
>>>
>>>Yes. The major problem that we have in modelling brain processes is
>>>the complexity of the whole thing. I mean, our supercomputers have
>>>problems with 2-3000 neuron models. Massively parallel systems
>>>reach the 16-32000 neuron level. How much of the brain is actually
>>>dedicated to thought? Maybe what, 10^10 neurons?
>>>
>> I recall reading we'd need a computer the size of (something like)
>> Manhatten to approximate the brain. Does that sound like a resonable
>> size?
>
>Well, I just did some quick calculations, and came up with over three
>hundred million of the new Thinking Machines CM-5 (I think that's the
>model.) Stacked three high, that's a solid block about three miles long
>on each side. But then you need power supplies, cooling systems, and lots
>of cabling.
>
>I suspect (in fact I'm sure) that this model is a bit overkill. Sounds
>impressive, but really overly useful. A whole SPARC processor for each
>neuron? Phooey. In practice, I think that Intel(?) has a prototype
>neural network chip that simulates about 400 connected neurons in parallel.
>Assuming liberal space requirements for each chip, that's only a block
>7/10 of a mile on each side, and twenty feet high. Of course, there are 
>a few minor implementation problems to be worked out...
>
Just a few.... In any case, we're still talking huge, giant,
not-exactly-a-laptop cases here...
Seems like these systems could do some fairly accurate modeling for
us. I wonder if the pro-life side would be willing to cough up the
bucks needed to build one and do some testing?

--
                  Mark Cochran          merlin@eddie.ee.vt.edu
These are the views of my employer, your employer, your government, the
Church of your choice, and the Ghost of Elvis. So there.
Member, T.S.A.K.C.
Newsgroups: talk.abortion