honavar@speedy.WISC.EDU (A Buggy AI Program) (10/15/87)
Computer Sciences Technical Report #717, September 1987. -------------------------------------------------------- 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. ----- Vasant Honavar honavar@speedy.wisc.edu