Sequential recognition of superimposed patterns with top-down selective attention

Top-down attention is a cognitive mechanism to filter out irrelevant information from sensory input. Unlike bottom-up attention based on the sensory signal itself the top-down attention process is originated from the higher brain, which consists of previous knowledge about the sensory signals. A simple computational model is developed for the top-down attention. In this model an attention gain coefficient is assigned to each input feature, and all the attention gain coefficients are dynamically adjusted based on previous knowledge. A multilayer Perceptron is used to model the knowledge in the higher brain. The developed model demonstrates excellent capability of extracting and recognizing each pattern sequentially from superimposed dual-class patterns studied in visual perception. (C) 2004 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2004-06
Language
ENG
Citation

NEUROCOMPUTING, v.58, pp.633 - 640

ISSN
0925-2312
URI
http://hdl.handle.net/10203/10221
Appears in Collection
EE-Journal Papers(저널논문)
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