University of Saskatchewan, 112 Science Pl, Saskatoon, SK, S7N 5C8, Canada.
University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, S7N 5A9, Canada.
Biol Cybern. 2021 Jun;115(3):245-265. doi: 10.1007/s00422-021-00876-8. Epub 2021 May 16.
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.
looming 障碍物的检测对于自然和人工系统都是至关重要的任务。蝗虫具有视觉神经系统,其障碍物检测途径得到了广泛研究,最终导致了 lobula giant movement detector (LGMD) 神经元的产生。虽然这个系统存在许多模型,但迄今为止,没有一个模型结合了最近关于 LGMD 输入网络中前馈和全局抑制系统的解剖结构和功能的数据。此外,全局和侧向抑制是否会影响到 LGMD 的前馈抑制信号,这一点尚未得到研究。为了解决这些问题,根据最近的文献,我们开发了一个蝗虫复眼叶中前馈抑制神经元的新模型。该模型还根据它们在现有数据中的观察行为以及这些机制在 LGMD 输入中的假定作用,将全局和侧向抑制纳入这些神经元的传入网络。对模型的测试表明,它可以准确地复制蝗虫中前馈抑制神经元的行为;该模型以整体线性方式准确地对刺激角大小进行编码,随着刺激大小的增加或接近速度的降低,响应饱和度降低,线性度增加。该模型对光栅的出现也只表现出相位响应,并且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,可以确定全局抑制主要用于在接近碰撞时对不断增加的兴奋进行归一化,并保持对亚尺寸角的线性编码。侧向抑制被确定为抑制以恒定速度平移的宽场刺激的紧张反应。基于这些特征与 LGMD 输入网络的特征相似,假设前馈抑制神经元和 LGMD 共享相同的兴奋性传入;这需要进一步的研究。