在杂乱运动背景下解码平移物体方向的果蝇运动视觉通路模型
Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds
摘要：
在杂乱的运动背景中准确有效地解码平移对象的方向仍然是一个具有挑战性的问题。在自然界中，轻量级和低功率的飞行昆虫在飞行过程中应用运动视觉来检测高度可变环境中的运动目标，这是学习运动感知策略的极好范例。本文研究了果蝇\textit{Drosophila}运动视觉通路，并提出了基于前沿生理研究的计算建模。建议的视觉系统模型功能生物合理的ON和OFF途径，宽场水平敏感（HS）和垂直敏感（VS）系统。本研究的主要贡献在于两个方面：1）提出的模型以前馈方式描述了运动知觉神经回路的主要特征--方向选择（DS）和方向对抗（DO）反应的形成; 2）它还显示出对在杂乱移动背景前面平移对象的鲁棒方向选择性，通过时空动态的建模，包括运动预过滤机制和ON和OFF路径内的局部干扰器的集合的组合，其有效地工作以抑制不相关的背景运动或干扰物，并改善动态响应。因此，平移对象的方向被解码为HS和VS系统两者的全局响应，其中正或负输出指示优选方向（PD）或零方向（ND）平移。实验验证了所提出的神经系统模型的有效性，并证明了其响应偏好的快速移动，高对比度和大尺寸的目标嵌入在杂乱的运动背景。
英文摘要：
Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent paradigms to learn motion perception strategies. This paper investigates the fruit fly \textit{Drosophila} motion vision pathways and presents computational modelling based on cutting-edge physiological researches. The proposed visual system model features bio-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. The main contributions of this research are on two aspects: 1) the proposed model articulates the forming of both direction-selective (DS) and direction-opponent (DO) responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; 2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response. Accordingly, the direction of translating objects is decoded as global responses of both the HS and VS systems with positive or negative output indicating preferred-direction (PD) or null-direction (ND) translation. The experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets embedded in cluttered moving backgrounds.
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