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[Ray White]

   This notice is to announce a short paper which will be presented
at IJCNN-91 Seattle.

		       COMPETITIVE HEBBIAN LEARNING

			      Ray H. White

		Departments of Physics and Computer Science

			  University of San Diego

				Abstract

	  Of crucial importance for applications of unsupervised learning
	to systems of many nodes with a common set of inputs is how the
	nodes may be trained to collectively develop optimal response to
	the input. In this paper Competitive Hebbian Learning, a modified
	Hebbian-learning rule, is introduced. In Competitive Hebbian
	Learning the change in each connection weight is made proportional
	to the product of node and input activities multiplied by a factor
	which decreases with increasing activity on the other nodes. The
	individual nodes learn to respond to different components of the
	input activity while collectively developing maximal response.
	Several applications of Competitive Hebbian Learning are then
	presented to show examples of the power and versatility of this
	learning algorithm.

This paper has been placed in Jordan Pollack's neuroprose archive at Ohio
State, and may be retrieved by anonymous ftp. The title of the file
there is 

	white.comp-hebb.ps.Z

and it may be retrieved by the usual procedure:

local> ftp cheops.cis.ohio-state.edu (or ftp 128.146.8.62)
Name(128.146.8.62:xxx) anonymous
password: neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get white.comp-hebb.ps.Z
ftp> quit
local> uncompress white.comp-hebb.ps.Z
local> lpr -P(your_local_postscript_printer) white.comp-hebb.ps

Ray White (white at teetot.acusd.edu or white at cogsci.ucsd.edu)