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
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;
Journal_Title :
Neural Networks, IEEE Transactions on