DocumentCode
1242114
Title
Locally excitatory globally inhibitory oscillator networks
Author
Wang, DeLiang ; Terman, David
Author_Institution
Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA
Volume
6
Issue
1
fYear
1995
Firstpage
283
Lastpage
286
Abstract
A novel class of locally excitatory, globally inhibitory oscillator networks (LEGION) is proposed and investigated. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. In the network, an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing other oscillators from jumping up. Computer simulations demonstrate that the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. This model lays a physical foundation for the oscillatory correlation theory of feature binding and may provide an effective computational framework for scene segmentation and figure/ground segregation in real time.<>
Keywords
correlation theory; neural nets; oscillators; pattern recognition; relaxation theory; synchronisation; LEGION; connected regions; desynchronization; feature binding; figure/ground segregation; locally excitatory globally inhibitory oscillator networks; oscillatory correlation theory; scene segmentation; synchronization; two-time-scale standard relaxation oscillator; Computer simulation; Computer vision; Encoding; Humans; Layout; Local oscillators; Object recognition; Physics computing; Recruitment; Scattering;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.363423
Filename
363423
Link To Document