PDF(Pubmed)
Abstract：
Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust\'s visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model\'s performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments.
摘要：
基于蝗虫视觉大脑中小叶巨运动探测器（LGMD）的生物启发模型在各种场景下的碰撞感知中受到了广泛的关注和应用。这些模型在视觉处理中具有低功耗和高计算效率等优点。然而,当前基于LGMD的计算模型，通常组织为四层神经网络，经常遇到与嘈杂信号有关的挑战，特别是在复杂的动态环境中。生物学研究揭示了突触传递的内在随机性，可以帮助神经计算减轻噪音。与这些生物学发现一致，本文介绍了一种概率LGMD（Prob-LGMD）模型，该模型将概率纳入多个层之间的突触连接中，从而捕获信号传输中的不确定性，互动,和神经元之间的整合。使用一系列视觉刺激对拟议的Prob-LGMD模型和两个常规LGMD模型进行了比较测试，包括室内结构化场景和复杂的室外场景，都受到人为噪音的影响。此外,将该模型的性能与标准工程噪声过滤方法进行了比较。结果清楚地表明，所提出的模型优于所有比较方法，表现出显着改善的噪声耐受性。这项研究展示了一种简单而有效的方法来增强嘈杂环境中的碰撞感知。
