Neural computing ideas and biological terms

[Robert.Kentridge@durham.ac.uk]

Rogene Eichler writes:

> However, I grow tired of defending the validity of models to biologists
> who do not seem satisfied with any model that does not capture every last
> nuiance of complexity or that does not explain every last experimental
> finding. Modeling the brain will provide valuable insights into how we
> process information and how we can exploit those rules for artificial 
> systems. But they do not need to duplicate every last brain dynamic to
> be useful or valid. And when modelers continue to make the claim 'just
> like the brain' for the sake of convincing you of the validity of the 
> model, they are reinforcing the claim that the brain is the ONLY measure.


	I think a distinction can be drawn here between models which
are simplifications of known biology but which don't include
biological impossibilities and models which are simple and
biologically impossible.  Of course, this distinction might be a
little in the eye of the beholder, for example I'd argue that, in a
network, single compartment neurons are acceptable simplifications
which still allow one to draw some conclusions about information
processing in biological neural networks, but I know people who would
disagree.  On the other hand its pretty hard to argue that back-prop
is any kind of simplification of biology.

	Of course, this assumes that your interest is in finding out
about biology.  If you just want to use networks in their own right
then fine (but be wary of leaning on biology too much to justifiy
their design!)

				cheers,
					bob

-- 
	Dr. R.W. Kentridge	phone: +44 91 374 2621
	Psychology Dept.,	email: robert.kentridge at durham.ac.uk
	University of Durham,
	Durham DH1 3LE, U.K.