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[Steven J. Nowlan]

I usually avoid free-wheeling network discussions such as this, but I
believe that Steve Lehar is doing Jim Bower an injustice in his 
characterization of Jim's argument:
 
| In  his  final communication Jim  Bower  strikes at   the heart of his
| differences  with  biological  connectionist philosophy.   While  many
| connectionists believe that their paradigm bears both a structural and
| functional   similarity  to   brain and  mind, and  is  thus a   valid
| theoretical tool for   exploring those entities, Bower  believes  that
| connectionism is  no     closer to   understanding   the  brain   than
| conventional  AI  or  any     other   paradigm.    My      "180-degree
| misunderstanding"  of his  former posting was (I am  left to guess) in
| thinking  that he  opposed ALL theoretical modeling,   whereas what he
| opposes is all theoretical modeling of the BRAIN.
| 
| It seems that Bower  is ferverently convinced  that the  mechanisms of
| the   brain  are a deep dark  secret  that will   not  yield to simple
| investigations with numerical models.


My own (admittedly limited) understanding of the crux of Jim's argument might 
be summarized as follows:

 The idea that "a neuron functions by emitting action potentials proportional
 to a non-linear squashing function applied to the total activity received
 through its synaptic connections with other neurons" is at least as far from
 the truth as the idea that "a neuron represents a logical proposition."

This strikes me as a reasonable statement, given what little we do know about
the incredible complexity of neuronal function. 

I think Jim's point is important to bear in mind, because it (should) keep us
from attempting to justify a connectionist model of some phenomena simply (or
mainly) because it is "more brain like" than some other abstract model. This
sort of reasoning is tempting, and places one on very shaky scientific ground.
It is all too easy to develop some pet theory of how X is done, design some
network model based on this theory, simulate the model and exclaim "Aha, this
model supports theory Y about X and is a network -- so theory Y must explain
how the brain does X". Since the assumptions of the theory were built into
the model in the first place, the simulations may in fact tell us very little.

Our models and theories need to be tested in the time honored way -- by
considering what predictions the theories make and attempting to design
critical experiments which will support or refute these predictions.

This is not to say that connectionist modelling has nothing to say to the
experimental biologist. I think a very good example of what it has to say
can be seen in some of Sean Lockery's work on the leech bending reflex. What
this work suggests is that single cell recordings of isolated neurons, and 
analysis of the synaptic organization of individual neurons is not likely to 
be very fruitful for understanding the functional role of these neurons
because real biological neurons appear to share a computational property of
connectionist models -- the functional role of any unit cannot be understood
in isolation but only in the context of the functioning of other computational
units. 

Given the current state of development of connectionist models, and
understanding of biological neuronal processing, it seems that
cross-fertilization of ideas is likely to be most effective at this rather
abstract level of computational properties.

		- Steve