A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception

2026-09-09

AI Summary

为解决现有模型在复杂动态视觉环境中的生物合理性和鲁棒性问题，提出了一种仿生蝗虫视觉神经网络，采用空间均匀采样策略、群体投票机制和漏电积分-放电神经元动力学方法。

Abstract

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.

Problem

• Looming Perception
• Collision Avoidance
• Biological Plausibility
• Robustness

Innovation

• spatially isotropic sampling
• population-voting mechanism
• leaky integrate-and-fire neuronal dynamics