Biologically Plausible Dynamic Artificial Neural Networks

Joseph T. Devlin jdevlin at pollux.usc.edu
Mon Jan 11 14:42:25 EST 1993

Ulf Andrick  writes:
>I just wanted to counter the view that Artificial Neural
>Networks (ANN) are suitable to explain everything in the brain.

  I think I can be fairly confident when I say that no-one
really suggests that ANNs might explain "everything in the
brain" - that'd be a neat trick!  It'd put us all out jobs,
>Further more, the posting I answered to referred to small
>neural systems in simple organisms, and here, I don't see a
>field for the application of ANN. I think of the stomatogastric
>ganglion of the crab or the flight generator of the locust
>when talking about small neural systems. 

  I think this depends on what exactly you are referring to
when you say "Artificial Neural Net (ANN)".  If you mean any
computational model of neural activity then certainly
Selverston's work at UCSD qualifies as a small ANN in a simple
organism (the lobster stomatoganglion system).
  If, on the other hand, you mean solely the more traditional
ANNs such as the models in McClelland and Rumelhart's PDP book
then I would agree.  These types of models seem to provide no
real insight into detailed neural systems that are fairly well
characterized biologically but I don't believe they were intended
to, either.  PDP models are more useful for modeling cognitive
issues where the underlying biology is as yet unknown but 
nonetheless the modeler would like to capture general components
of the biology - such as distributed representation, massive
parallelism, etc.  As it stands there is certainly debate concerning
the usefullness of these models - see the ongoing McCloskey/Seidenberg
debate - but  I like Seidenberg's arguments which I think are very
elegant (but I work in his lab so I'm biased. :-) 

							- Joe

Joseph Devlin                      * email: jdevlin at pollux.usc.edu
University of Southern California  *
Department of Computer Science     * "The axon doesn't think.
Los Angeles, CA 90089              *  It just ax."  George Bishop

  McClelland and Rumelhart (1986) _Parallel Distributed Processing_, MIT Press.

  McCloskey (1992) Networks and theories: The place of connectionism in cognitive
science. _Psychogical Science_

  Rowat & Selverston (1991) Learning algorithms for oscillatory networks with
gap junctions and membrane currents.  _Networks 2_, 17-41.

  Seidenberg (in press) [A response to McCloskey...] _Psychological Science_

Note: The references are from memory basically so I apologize for any
      inaccuracies in advance.  I just can't remember the title of the
      Seidenberg paper - my copy doesn't have one.

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