Wang D, Terman D
Dept. of Comput. and Inf. Sci., Ohio State Univ., Columbus, OH.
IEEE Trans Neural Netw. 1995;6(1):283-6. doi: 10.1109/72.363423.
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.
提出并研究了一类新型的局部兴奋性、全局抑制性振荡器网络（LEGION）。每个振荡器的模型对应于一个具有两个时间尺度的标准弛豫振荡器。在网络中，一个迅速跃升至其活跃相位的振荡器会迅速招募受相同模式刺激的振荡器，同时阻止其他振荡器跃升至活跃相位。计算机模拟表明，该网络能够迅速在由相连区域刺激的振荡器块内实现同步，并在不同块之间实现去同步。该模型为特征绑定的振荡相关理论奠定了物理基础，并可能为实时场景分割和图形/背景分离提供一个有效的计算框架。
