IUBio

Connectionist Learning - Some New Ideas

Jeff Baldwin mvcs at gramercy.ios.com
Mon May 27 01:43:13 EST 1996


To: Daniel Crespin, dcrespin at gauss.ciens.ucv.ve

These comments are made to the post you submitted by the above title on 
23 May 1996 19:05:35 +0100. I have refrained from including that post 
here. You said:

># Comment (3 of 5) to C: ....snip.... There does not seem to be an 
>obvious criterion to tell when the extra examples are waste.

Three obvious criteria to tell "when the extra examples are wasted" (for 
the example pattern classification problem posed herein):
a) Replicates of examples.
b) Examples which over-define boundary conditions. It takes only two 
points to define a linear boundary condition. Two boundaries may share a 
common boundary-specification-example. Any examples which fall on a 
boundary once it is defined are superfluous.
c) Interior points. Once all boundaries of the polygon is defined, 
interior points are superfluous.

Now, there seems to be angst expressed here towards algorithm designers 
for their perceived abandonment of the constraint to honor 
true-biological-rules during algorithm design. However, real brains work 
mostly in the frequency domain space. Yet, the arguments here seem to 
assume non-frequency-based NN i/o algorithms as the only extant 
simulation modes. Shall the designers of mere pragmatic, engineering-like 
algorithms be here judged by a standard to which this research has yet to 
aspire?

Does the angst here stem from a bio-purist attitude? If so, it seems 
discriminatory and expresses a degree of bio-elitism which is frankly 
surprising. 

If the goal of this work is modeling of biophysical processes utilized 
during human experiences, then the purist approach may be well advised. 
"Toy" applications such as presented here in the simple A vs B class 
discrimination task are well posed mathematically. Yet to generalize such 
simplistic mathematical treatment to all purviews of simulated neural 
network applications and thereby also require all generalizations to 
adhere strictly to supposed biophysical rules misses the pragmatic mark. 
The simplest implementation of a simulated neural network system may only 
need to be _inspired_ by an observation of biophysical reality; it need 
not be a true emulation of bio-reality in order to be useful. Perhaps a 
name change will suffice to separate the wheat from the chaff? Perhaps 
"Simulated Pseudo-Neural Networks" or somesuch?

I fully expect that whatever is learned at _every_ stage of the research 
to which Dr. Roy eludes will in some way find application to some 
stand-alone or semi-conventional processing system of some type. The 
ideas and discoveries will be used in every pragmatic way possible to 
produce whatever is desired, and whatever is important enough to warrant 
the effort. As for myself, I await the application of Dr. Roy's ideas to 
the frequency-domain reality of neuron transfer functions and the 
implicit, manifold methods of information processing effected by the 
excitation and inhibition mechanisms composed in the "ionic soup" which 
fills all the void space between neurons, synapses, dendrites, et al. In 
the mean time, it's back to the application of simulated pseudo-neural 
memories, pattern classifiers, optimizers, and hybrid systems to my 
visually-driven and memory intensive problem domain.

Cheers,
-Jeff Baldwin
Mind&Vision Computer Systems




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