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首页> 外文会议>Biomedical Applications of Micro- and Nanoengineering II; Progress in Biomedical Optics and Imaging; vol.5 no.34 >Modelling pattern noise in responses of fly motion detectors to naturalistic scenes
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Modelling pattern noise in responses of fly motion detectors to naturalistic scenes

机译：模拟飞行运动探测器对自然场景的响应中的模式噪声

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摘要

Insects have a very efficient visual system that helps them to perform extraordinarily complicated navigational acts and precisely controlled acrobatic flight. Physiological evidence suggests that flight control is guided by a small system of 'tangential' neurons tuned to very specific types of complex motion by the way that they collate information from local motion detectors. One class of tangential neurons, the 'horizontal system' (HS) neurons, respond with opponent graded responses to yaw stimuli. Using the results of physiological experiments, we have developed a model, based on an array of Reichardt correlators, for the receptive field of HS neurons that view optical flow along the equator. Our model incorporates additional non-linearities that mimic known properties of the insect motion pathway, including logarithmic encoding of luminance, saturation and motion adaptation (adaptive gain-control). In this paper, we compare the response of our elaborated model with fly HS neuron responses to naturalistic image panoramas. Such responses are dominated by noise which is largely non-random. Deviations in the correlator response are likely due to the structure of the visual scene, which we term "Pattern noise". To investigate the influence of anisotropic features in producing pattern noise, we presented a panoramic image at various initial positions, and versions of the same image modified to disrupt vertical contours. We conclude that the response of the fly neurons shows evidence of local saturation at key stages in the motion pathway. This saturation reduces the effect of pattern noise and improves the coding of velocity. Our model provides an excellent basis for the development of biomimetic yaw sensors for robotic applications.
机译：昆虫具有非常有效的视觉系统，可帮助它们执行异常复杂的导航操作并精确控制杂技飞行。生理证据表明，飞行控制由“切向”神经元的小型系统引导，该系统通过对来自本地运动检测器的信息进行整理的方式，将其调整为非常特定类型的复杂运动。一类切线神经元，即“水平系统”（HS）神经元，会以对手对偏航刺激的分级反应来响应。利用生理学实验的结果，我们基于一系列的Reichardt相关器，开发了一个模型，用于观察沿赤道的光流的HS神经元的感受野。我们的模型结合了模仿昆虫运动路径已知特性的其他非线性，包括亮度，饱和度和运动适应性（自适应增益控制）的对数编码。在本文中，我们将精细模型的响应与对自然主义图像全景图的苍蝇HS神经元响应进行了比较。这样的响应主要是噪声，它是非随机的。相关器响应中的偏差很可能归因于视觉场景的结构，我们称其为“模式噪声”。为了研究各向异性特征在产生图案噪声中的影响，我们在各个初始位置展示了全景图像，并对同一图像的版本进行了修改以破坏垂直轮廓。我们得出结论，果蝇神经元的反应显示出运动通路关键阶段局部饱和的证据。这种饱和降低了图案噪声的影响，并改善了速度编码。我们的模型为开发机器人应用的仿生偏航传感器提供了极好的基础。

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