基于ON和OFF路径分离的方向选择神经网络在视觉混乱环境中的平移运动感知
A Directionally Selective Neural Network with Separated ON and OFF Pathways for Translational Motion Perception in a Visually Cluttered Environment
摘要：
根据近十年来果蝇生理学的研究成果，我们提出了一种具有前馈结构和完全低层次视觉处理的方向选择神经网络，以实现果蝇视觉系统中对四个方向的宽场平移运动敏感的方向选择神经元。在这项研究中，我们强调了ON和OFF路径的功能，分离运动信息，用于对应于光的开和关选择性的并行计算。通过这种建模研究，我们展示了一些成果相比，以前的生物合理的平移运动检测器，如基本的运动检测器。首先，我们彻底模仿苍蝇的初步运动检测途径与新发现的苍蝇的生理。其次，我们通过在双通道中设计相同极性的细胞系综来提高对移动暗/亮特征的速度响应。此外，我们减轻了不相关的运动在视觉上杂乱的环境，如背景和风吹植被的变化，通过时空动态建模的影响。我们系统地测试了DSNN对刺激范围从合成和现实世界的场景，特别是地面微型机器人的视觉模态。结果表明，DSNN优于以前的生物合理的平移运动检测器。重要的是，我们验证了其计算的简单性和有效性，有利于机器人的神经形态视觉传感器的建设。
英文摘要：
With respect to biological findings underlying fly's physiology in the past decade, we present a directionally selective neural network, with a feed-forward structure and entirely low-level visual processing, so as to implement direction selective neurons in the fly's visual system, which are mainly sensitive to wide-field translational movements in four cardinal directions. In this research, we highlight the functionality of ON and OFF pathways, separating motion information for parallel computation corresponding to light-on and light-off selectivity. Through this modeling study, we demonstrate several achievements compared with former bio-plausible translational motion detectors, like the elementary motion detectors. First, we thoroughly mimic the fly's preliminary motion-detecting pathways with newly revealed fly's physiology. Second, we improve the speed response to moving dark/light features via the design of ensembles of same polarity cells in the dual-pathways. Moreover, we alleviate the impact of irrelevant motion in a visually cluttered environment like the shifting of background and windblown vegetation, via the modeling of spatiotemporal dynamics. We systematically tested the DSNN against stimuli ranging from synthetic and real-world scenes, to notably a visual modality of a ground micro robot. The results demonstrated that the DSNN outperforms former bio-plausible translational motion detectors. Importantly, we verified its computational simplicity and effectiveness benefiting the building of neuromorphic vision sensor for robots.
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