• 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