Paper in neuroprose: Learning In Neural Models With Complex Dynamics

[Dr. Michael D. Stiber]

The following preprint has been placed in the Neuroprose archives at
Ohio State (filename: stiber.dynlearn.ps.Z).  If you cannot use FTP, I
can email the file to you.

	  "Learning In Neural Models With Complex Dynamics" (4 pages)

			    Michael Stiber
		    Department of Computer Science
	  The Hong Kong University of Science and Technology
		 Clear Water Bay, Kowloon, Hong Kong
			   stiber at cs.ust.hk
				   
			   Jose P. Segundo
		Department of Anatomy and Cell Biology
		     and Brain Research Institute
		       University of California
		  Los Angeles, California 90024, USA
		       iaqfjps at mvs.oac.ucla.edu

			       Abstract

Interest in the ANN field has recently focused on dynamical neural
{\em networks} for performing temporal operations, as more realistic
models of biological information processing, and to extend ANN
learning techniques. While this represents a step towards realism, it
is important to note that {\em individual} neurons are complex
dynamical systems, interacting through nonlinear, nonmonotonic
connections. The result is that the ANN concept of {\em learning},
even when applied to a single synaptic connection, is a nontrivial
subject.

Based on recent results from living and simulated neurons, a first
pass is made at clarifying this problem. We summarize how synaptic
changes in a 2-neuron, single synapse neural network can change system
behavior and how this constrains the type of modification scheme that
one might want to use for realistic neuron-like processors.

Dr. Michael Stiber					stiber at cs.ust.hk
Department of Computer Science				tel: (852) 358 6981
The Hong Kong University of Science & Technology	fax: (852) 358 1477
Clear Water Bay, Kowloon, Hong Kong