Borisyuk Roman, Kazanovich Yakov, Chik David, Tikhanoff Vadim, Cangelosi Angelo
School of Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK.
Neural Netw. 2009 Jul-Aug;22(5-6):707-19. doi: 10.1016/j.neunet.2009.06.047. Epub 2009 Jul 7.
A brain-inspired computational system is presented that allows sequential selection and processing of objects from a visual scene. The system is comprised of three modules. The selective attention module is designed as a network of spiking neurons of the Hodgkin-Huxley type with star-like connections between the central unit and peripheral elements. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics corresponds to the partial synchronization mode. It is shown that peripheral neurons with higher firing rates are preferentially drawn into partial synchronization. We show that local excitatory connections facilitate synchronization, while local inhibitory connections help distinguishing between two groups of peripheral neurons with similar intrinsic frequencies. The module automatically scans a visual scene and sequentially selects regions of interest for detailed processing and object segmentation. The contour extraction module implements standard image processing algorithms for contour extraction. The module computes raw contours of objects accompanied by noise and some spurious inclusions. At the next stage, the object segmentation module designed as a network of phase oscillators is used for precise determination of object boundaries and noise suppression. This module has a star-like architecture of connections. The segmented object is represented by a group of peripheral oscillators working in the regime of partial synchronization with the central oscillator. The functioning of each module is illustrated by an example of processing of the visual scene taken from a visual stream of a robot camera.
提出了一种受大脑启发的计算系统，该系统允许从视觉场景中顺序选择和处理对象。该系统由三个模块组成。选择性注意模块被设计为一个霍奇金 - 赫胥黎类型的脉冲神经元网络，中央单元和外围元素之间具有星状连接。注意焦点由那些与中央神经元同步产生脉冲的外围神经元表示，而其他外围神经元的活动受到抑制。这种动态对应于部分同步模式。结果表明，具有较高 firing 率的外围神经元优先被吸引到部分同步中。我们表明，局部兴奋性连接促进同步，而局部抑制性连接有助于区分两组具有相似固有频率的外围神经元。该模块自动扫描视觉场景，并顺序选择感兴趣区域进行详细处理和对象分割。轮廓提取模块实现用于轮廓提取的标准图像处理算法。该模块计算伴有噪声和一些虚假包含物的对象的原始轮廓。在下一阶段，设计为相位振荡器网络的对象分割模块用于精确确定对象边界并抑制噪声。该模块具有星状连接架构。分割后的对象由一组与中央振荡器在部分同步状态下工作的外围振荡器表示。每个模块的功能通过处理来自机器人相机视觉流的视觉场景的示例进行说明。
