honavar@speedy.WISC.EDU (A Buggy AI Program) (10/15/87)
Computer Sciences Technical Report #717, September 1987.
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RECOGNITION CONES: A NEURONAL ARCHITECTURE FOR
PERCEPTION AND LEARNING
Vasant Honavar, Leonard Uhr
Computer Sciences Department
University of Wisconsin-Madison
Madison, WI 53706. U.S.A.
ABSTRACT
There is currently a great deal of interest
and activity in developing connectionist, neu-
ronal, brain-like models, in both Artificial
Intelligence and Cognitive Science. This paper
specifies the main features of such systems,
argues for the need for, and usefulness of struc-
turing networks of neuron-like units into succes-
sively larger brain-like modules, and examines
"recognition cone" models of perception from this
perspective, as examples of such structures.
Issues addressed include architecture, information
flow, and the parallel-distributed nature of pro-
cessing and control in recognition cones; and
their use in perception and learning.
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Vasant Honavar
honavar@speedy.wisc.edu