Detection of looming obstacles is a vital task for both natural and artificial systems. Locusts possess a visual nervous system with an extensively studied obstacle detection pathway, culminating in the lobula giant movement detector (LGMD) neuron. While numerous models of this system exist, none to date have incorporated recent data on the anatomy and function of feedforward and global inhibitory systems in the input network of the LGMD. Moreover, the possibility that global and lateral inhibition shape the feedforward inhibitory signals to the LGMD has not been investigated. To address these points, a novel model of feedforward inhibitory neurons in the locust optic lobe was developed based on the recent literature. This model also incorporated global and lateral inhibition into the afferent network of these neurons, based on their observed behaviour in existing data and the posited role of these mechanisms in the inputs to the LGMD. Tests with the model showed that it accurately replicates the behaviour of feedforward inhibitory neurons in locusts; the model accurately coded for stimulus angular size in an overall linear fashion, with decreasing response saturation and increasing linearity as stimulus size increased or approach velocity decreased. The model also exhibited only phasic responses to the appearance of a grating, along with sustained movement by it at constant speed. By observing the effects of altering inhibition schemes on these responses, it was determined that global inhibition serves primarily to normalize growing excitation as collision approaches, and keeps coding for subtense angle linear. Lateral inhibition was determined to suppress tonic responses to wide-field stimuli translating at constant speed. Based on these features being shared with characterizations of the LGMD input network, it was hypothesized that the feedforward inhibitory neurons and the LGMD share the same excitatory afferents; this necessitates further investigation.

检测迫在眉睫的障碍物对于自然系统和人工系统来说都是一项至关重要的任务。蝗虫拥有视觉神经系统，其具有广泛研究的障碍检测途径，最终形成小叶巨动检测器 (LGMD) 神经元。虽然存在该系统的许多模型，但迄今为止还没有将有关前馈和全局抑制系统的解剖结构和功能的最新数据纳入 LGMD 的输入网络。此外，尚未研究全局和侧向抑制形成对 LGMD 的前馈抑制信号的可能性。为了解决这些问题，基于最近的文献开发了蝗虫视叶中前馈抑制神经元的新模型。该模型还将全局和侧向抑制纳入这些神经元的传入网络中，基于他们在现有数据中观察到的行为以及这些机制在 LGMD 输入中的假定作用。对该模型的测试表明，它准确地复制了蝗虫前馈抑制神经元的行为；该模型以整体线性方式准确编码刺激角大小，随着刺激大小增加或接近速度降低，响应饱和度降低，线性增加。该模型还仅表现出对光栅外观的相位响应，以及它以恒定速度持续运动。通过观察改变抑制方案对这些反应的影响，确定全局抑制主要用于在碰撞接近时使不断增长的兴奋正常化，并保持对对角线性编码。横向抑制被确定为抑制对宽场刺激以恒定速度翻译的强直反应。基于这些特征与 LGMD 输入网络的特征共享，假设前馈抑制神经元和 LGMD 共享相同的兴奋性传入；这需要进一步调查。