Paper available: Context, uncertainty, multiplicity, & scale

[Jonathan Marshall]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/marshall.context.ps.Z

This paper is available via anonymous-ftp from the Neuroprose archive.  It
is scheduled to appear in Neural Networks 8(3).  This is a revision (April
1994) of the previously-distributed version (February 1993), with some new
sections.

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                 ADAPTIVE PERCEPTUAL PATTERN RECOGNITION
                   BY SELF-ORGANIZING NEURAL NETWORKS:
              CONTEXT, UNCERTAINTY, MULTIPLICITY, AND SCALE
                                     
                           JONATHAN A. MARSHALL
         Department of Computer Science, CB 3175, Sitterson Hall
     University of North Carolina, Chapel Hill, NC 27599-3175, U.S.A.
            Telephone 919-962-1887, e-mail marshall at cs.unc.edu

ABSTRACT.  A new context-sensitive neural network, called an "EXIN"
(excitatory+inhibitory) network, is described.  EXIN networks
self-organize in complex perceptual environments, in the presence of
multiple superimposed patterns, multiple scales, and uncertainty.  The
networks use a new inhibitory learning rule, in addition to an excitatory
learning rule, to allow superposition of multiple simultaneous neural
activations (multiple winners), under strictly regulated circumstances,
instead of forcing winner-take-all pattern classifications.  The multiple
activations represent uncertainty or multiplicity in perception and
pattern recognition.  Perceptual scission (breaking of linkages) between
independent category groupings thus arises and allows effective global
context-sensitive segmentation constraint satisfaction, and exclusive
credit attribution.  A Weber Law neuron-growth rule lets the network learn
and classify input patterns despite variations in their spatial scale.
Applications of the new techniques include segmentation of superimposed
auditory or biosonar signals, segmentation of visual regions, and
representation of visual transparency.

KEYWORDS.  Masking fields, Anti-Hebbian learning, Distributed coding,
Adaptive constraint satisfaction, Decorrelators, Excitatory+inhibitory
(EXIN) learning, Transparency, Segmentation.

46 pages.

Thanks to Jordan Pollack for maintaining the Neuroprose archive!

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