AI 中文总结
针对现有蝗虫碰撞感知模型生物似然性和鲁棒性不足，提出一种融合各向同性采样、群体投票和泄漏积分发放动力学的生物似然神经网络，在多种场景下提升鲁棒性并保持高效。
AI 中文摘要
蝗虫视觉系统长期以来一直是研究逼近感知和碰撞规避的重要生物范式。众多计算模型已成功复现了小叶巨型运动检测器（LGMD）神经元对接近物体的选择性响应，从而模拟了该生物系统的基本功能。然而，现有模型在复杂动态视觉环境中运行时，其生物似然性和鲁棒性仍存在局限。为解决这些局限，我们提出了一种用于蝗虫启发的逼近检测的生物似然神经网络。该框架包含模拟蝗虫复眼小眼组织结构的空间各向同性采样策略、受生物神经系统群体编码启发的群体投票机制，以及用泄漏积分发放神经元动力学替代传统基于Sigmoid的膜激活。在合成刺激、实验室序列和真实驾驶场景上的系统性实验表明，所提模型在挑战性视觉条件下提高了鲁棒性，同时保持了计算效率并增强了生物保真度。这些结果凸显了基于生物学的神经计算在鲁棒高效碰撞感知方面的潜力。
英文摘要
Locust visual systems have long served as an important biological paradigm for studying looming perception and collision avoidance. Numerous computational models have successfully reproduced the selective responses of Lobula Giant Movement Detector (LGMD) neurons to approaching objects, thereby emulating the fundamental functionality of the biological system. However, existing models remain limited in biological plausibility and robustness when operating in complex and dynamic visual environments. To address these limitations, we propose a biologically plausible neural network for locust-inspired looming detection. The proposed framework incorporates a spatially isotropic sampling strategy that mimics the ommatidial organization of the locust compound eye, a population-voting mechanism inspired by population coding in biological neural systems, and leaky integrate-and-fire neuronal dynamics to replace conventional sigmoid-based membrane activation. Systematic experiments on synthetic stimuli, laboratory sequences, and real-world driving scenarios demonstrate that the proposed model improves robustness under challenging visual conditions while preserving computational efficiency and enhancing biological fidelity. These results highlight the potential of biologically grounded neural computation for robust and efficient collision perception.
Comments6 pages, 8 figures, conference
