Neuronal basis of visually-guided insect behaviour and effects of neonicotinoid pesticides

视觉引导昆虫行为的神经基础和新烟碱类农药的影响

• 批准号: RGPIN-2019-03983
• 负责人: Gray, John
• 金额: $ 3.42万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714461
• 关键词: Neuronal; basis; visually; guided; insect

Animal survival depends on the nervous system's ability to collect and process relevant information and generate appropriate behavioural responses. Animals may be further challenged by presence of environmental toxicants (e.g. pesticides), that specifically target activity in the nervous system, and even low doses may have profound effects on neuronal processing and behaviour. Understanding the neural control of behaviour requires studying orientation in three-dimensional space and how kinematics of underlying locomotory appendages are driven by relevant muscle activity, followed by experiments on how the nervous system detects environmental cues and drives locomotory circuits. Combined activity of groups of neurons is crucial for detecting and deciphering complex sensory cues in animals ranging from humans to insects. Identifying mechanisms by which groups of neurons detect relevant cues from complex stimuli will also help us to understand mechanisms of potential behavioural impairment from pesticide exposure. The basic biological information can also enhance the design of biologically-inspired artificial systems capable of adaptable, self-guided navigation. My laboratory uses insect flight as an accessible system to address fundamental questions linking neurobiology and behaviour. We use techniques that include combinations of: 1) high speed video to track body and wing movements during flight, 2) electrical recordings of muscles that drive the wings, 3) single, multichannel and intracellular neurophysiological recordings of single or multiple neurons involved in visual processing, 4) effects of exposure to neonicotinoid pesticides on visually-guided behaviour, and 5) a virtual-reality environment with computer modelling to emulate real visual motion. We study responses of identified neurons in the locust nervous system to behaviourally-relevant visual cues. We are interested in how visual cues are processed and used to produce flight steering manoeuvres. Flight is complex, requiring rapid detection and processing of complex sensory signals and is controlled by relatively few neurons, making it accessible to rigorous experimental manipulation. For example, we examine how identified visual neurons respond to multiple objects or objects that change motion trajectory, which may or may not represent a danger to the animal. The three short-term objectives in this proposal include: 1) Understanding how multiple neurons code visual information and guide behaviour, 2) How pesticides disrupt neural processing and impair ecologically important behaviours in locusts and bees, and 3) Modelling neural and behaviour data to design algorithms for use in computational models and autonomous robots. Our work will be important for understanding fundamental processes of the neural control of behaviour and how these processes are affected by external stressors, and can also inform control strategies for artificial systems that orient in challenging environments.

动物的生存取决于神经系统收集和处理相关信息并产生适当行为反应的能力。动物可能会受到环境毒物（如杀虫剂）的进一步挑战，这些毒物专门针对神经系统中的活动，即使是低剂量也可能对神经元的加工和行为产生深远的影响。了解行为的神经控制需要研究三维空间中的方向，以及相关肌肉活动如何驱动底层运动附属物的运动学，然后进行神经系统如何检测环境线索并驱动运动回路的实验。神经元组的组合活动对于检测和破译从人类到昆虫的动物中的复杂感觉线索至关重要。识别神经元群从复杂刺激中检测相关线索的机制也将有助于我们了解农药暴露导致的潜在行为损害机制。基本的生物信息还可以加强生物启发的人工系统的设计，使其能够适应，自我导航。 我的实验室使用昆虫飞行作为一个可访问的系统，以解决连接神经生物学和行为的基本问题。我们使用的技术包括以下组合：1）在飞行期间跟踪身体和翅膀运动的高速视频，2）驱动翅膀的肌肉的电记录，3）参与视觉处理的单个或多个神经元的单个、多通道和细胞内神经生理学记录，4）暴露于类烟碱杀虫剂对视觉引导行为的影响，以及5）具有计算机建模以仿真真实的视觉运动的虚拟现实环境。我们研究了蝗虫神经系统中识别出的神经元对行为相关视觉线索的反应。我们感兴趣的是视觉线索是如何处理和用于产生飞行操纵。飞行是复杂的，需要快速检测和处理复杂的感觉信号，并且由相对较少的神经元控制，这使得它可以进行严格的实验操作。例如，我们研究识别的视觉神经元如何响应多个物体或改变运动轨迹的物体，这可能会或可能不会对动物造成危险。该提案中的三个短期目标包括：1）了解多个神经元如何编码视觉信息并指导行为，2）杀虫剂如何破坏蝗虫和蜜蜂的神经处理并损害生态重要行为，以及3）对神经和行为数据进行建模，以设计用于计算模型和自主机器人的算法。我们的工作对于理解行为神经控制的基本过程以及这些过程如何受到外部压力的影响非常重要，并且还可以为在具有挑战性的环境中定位的人工系统提供控制策略。