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Proceedings Paper

Biologically inspired computation and learning in Sensorimotor Systems
Author(s): Daniel D. Lee; H. Sabastian Seung
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Paper Abstract

Networking systems presently lack the ability to intelligently process the rich multimedia content of the data traffic they carry. Endowing artificial systems with the ability to adapt to changing conditions requires algorithms that can rapidly learn from examples. We demonstrate the application of such learning algorithms on an inexpensive quadruped robot constructed to perform simple sensorimotor tasks. The robot learns to track a particular object by discovering the salient visual and auditory cues unique to that object. The system uses a convolutional neural network that automatically combines color, luminance, motion, and auditory information. The weights of the networks are adjusted using feedback from a teacher to reflect the reliability of the various input channels in the surrounding environment. Additionally, the robot is able to compensate for its own motion by adapting the parameters of a vestibular ocular reflex system.

Paper Details

Date Published: 14 November 2001
PDF: 8 pages
Proc. SPIE 4479, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV, (14 November 2001); doi: 10.1117/12.448341
Show Author Affiliations
Daniel D. Lee, Lucent Technologies/Bell Labs. (United States)
H. Sabastian Seung, Massachusetts Institute of Technology (United States)


Published in SPIE Proceedings Vol. 4479:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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