One last time

[Christof Koch]

Re. Steve Lehars distinction between biological connectionism and
computational neuroscience.


...
>  BIOLOGICAL  CONNECTIONISM: Using connectionist  ideas to explore the
> functional  architecture of specific   biological circuits  found in
> nature.  (Paying attention to the detailed structure of the brain as
>  biologists)
>
>
> COMPUTATIONAL NEUROSCIENCE: Exploring the  theoreticalpossibilities
> of  abstract   connectionist  architectures  for   their information
> representation and processing potential.
...


I, for once, disagree with these definitions.  The aim of computational
neuroscience  (CN) is to understand information processing, storage and
propagation in nervous systems, from nematodes to men.  Within CN,
theories can exist at many different levels of organization and
complexity, ranging from biophysical faithful models of propagation of
action potentials in axons (e.g. Hodgkin-Huxley and non-linear cable
theory) to much more abstract models of, say, the computations
underlying optical flow in visual cortex to even more abstract
connectionists models of visual information processing (e.g. shape-
from-shading) or higher-level cognitive operations. All these models are
constrained to a greater-or-lesser extent by neurobiological and
psychophysical data appropriate to their level of investigation. Thus, it
would not make sense to simulate the diffusion equation in single
dendritic spines when considering how we compute stereo acuity (we do
not have to simulate the laws governing current flowing through a
transistor when trying to understand the FFT algorithm).  Thus,
connectionists modelsQ-if appropriately mapped onto biologyQ-are a
part of CN.

Another distinction that can be made is between simplified and realistic
models. The Reichardt-correlation model of motion detection in insects
is a beuatiful instance of this. This model describes how the steady-
state optomotor response of the fly to moving stimuli at the formal
mathematical level and therefore captures the essential nonlinearity in
this computation. It even carries over to human short-range motion
system. Yet it specifies nothing about the implementation. This is for a
latter, more realistic model. On the other hand, we will never
understand the brain by building a huge detailed model of it, simulating
every neuron in great detail. Even if we could, this simulation would be
as complex and ill-understood as the brain itself. Thus, we need both
types of models. This is a point  NOT always appreciated by
experimentalists, whose frequent objection to a theory is ...it does not
explain my favorite observation XYZ... The point is, is this observation relevan
t towards understanding the specific computation considered?


For more details on this see our article  on  Computational
Neuroscience by Sejnowski, Churchland and Koch,   Science, 1988


Christof
koch at iago.caltech.edu


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