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Motor initiated expectation through top-down connections as abstract context in a physical world

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3 Author(s)
Matthew D. Luciw ; Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824 USA ; Juyang Weng ; Shuqing Zeng

Recently, it has been shown that top-down connections improve recognition in supervised learning. In the work presented here, we show how top-down connections represent temporal context as expectation and how such expectation assists perception in a continuously changing physical world, with which an agent interacts during its developmental learning. In experiments in object recognition and vehicle recognition using two types of networks (which derive either global or local features), it is shown how expectation greatly improves performance, to nearly 100% after the transition periods. We also analyze why expectation will improve performance in such real world contexts.

Published in:

2008 7th IEEE International Conference on Development and Learning

Date of Conference:

9-12 Aug. 2008