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From: jer@peora.UUCP (J. Eric Roskos)
Newsgroups: net.arch
Subject: Computational ability of houseflies
Message-ID: <2121@peora.UUCP>
Date: Tue, 29-Apr-86 09:04:26 EDT
Article-I.D.: peora.2121
Posted: Tue Apr 29 09:04:26 1986
Date-Received: Fri, 2-May-86 07:26:17 EDT
References: <3080@ncsu.UUCP>
Organization: Concurrent Computer Corporation, Orlando, Fl
Lines: 63

> With all the talk about performance metrics, consider this:
>
> How many MIPS does a single brain neuron have?
>
>
> I ask this because it seems we don't need to compute faster, but
> to compute better.  After all, brain cells have switching times in
> the MILLIsecond range.  How does the brain do it?  We should probably
> start small, so how about the question:
>
> Does any hardware currently exist that matches the real-time computational
> ability of a housefly?

Funny you should ask this... it's a very interesting subject.

The thing is, the brain doesn't seem to do computing the way current-day
machines do; in particular, it seems to contradict a lot of the
logic-based approaches to artificial intelligence.  Think about how people
do arithmetic operations, for example... they do it by table lookup!  Of
course, they also follow algorithms (add this column of 1-digit numbers,
put the "carry" on top of the next column, etc.), but the basic arithmetic
operations don't work the way they do in computers; at some early time
they memorized "two times two is four; three times two is six," etc., and
now recall these discrete facts whenever they do arithmetic.

Recent research seems to suggest that in general a lot of human
"computation" also works this way, with the interesting enhancement that,
if you think of it in terms of a table, table entries tend to "attract"
nearby guesses, so that from an approximation you get pulled into the
memorized answer. (Likewise, if you make an initial guess that is nearer
to another (wrong) answer, you may get pulled to that one instead and have
trouble finding the right answer as a result.) Very simple published
algorithms (albeit slow ones on a sequential machine) exist for modelling
simple forms of this operation, although other research has suggested that
a variety of specialized "functional units" exist in the brain which
aren't covered by that model. (Incidentally, some very interesting
research in cognitive psychology shows that some classes of problem
solving can be modeled in terms of n-dimensional spaces, and you can even
produce surprisingly unexpected artifacts of this spatiality -- for
example, people categorizing things using attributes of the objects that
are highly nonobvious, seemingly based entirely on this spatial distance
-- which of course really isn't spatial per se., probably, but is probably
an artifact of the number of partitions of the "bits" present which
are used to store the data.)

On the other hand, what does it mean for something to "compute better"?  A
lot of the things current-day computers do, people don't do so well -- for
example, memorizing and organizing extremely large numbers of very similar
things very quickly, performing fast numerical computation, etc.
Likewise, human beings tend to be more inexact, but also more
fault-tolerant (which is a property of the above model) and able to
perceive abstract properties of things (which in fact may be the result of
non-consequential thinking -- which runs somewhat counter to the way
people describe their thought processes, actually.  How many
mathematicians really admit "I was just sitting eating lunch and idly
thinking about how to prove this theorem, and suddenly it occurred to me
out of nowhere."?)

Nevertheless, these new devices based on neural research (there is an
article now almost every week on the subject in EE Times) are one of the
more interesting things going on today. (my opinion, of course!)
-- 
E. Roskos