State dependent neuronal encoding of complex sensory environments

大多数动物生活在复杂的环境中，这些环境往往包含着与周围环境相互矛盾的信息。因此，生存取决于神经系统收集和处理相关信息的能力，从而使动物产生适当的行为反应。虽然单个神经元可能适合于检测环境的某些方面，但神经元群的联合活动对于检测和破译复杂的感觉线索至关重要。从昆虫到人类等动物的常见检测方案表明，从环境中提取重要信息涉及类似的基本过程。此外，通过识别神经元群从复杂刺激中检测相关线索的机制，将有可能设计出更好的生物启发的人工系统，能够适应，自我引导导航。我的实验室研究可接近的昆虫神经系统中已识别的神经元对相关视觉和气味线索的反应。具体地说，我们感兴趣的是如何处理信息以生产飞行转向机动。飞行是一种复杂的行为，需要快速检测和处理复杂的感官信号。这种行为也由相对较少的神经元控制，使其易于进行严格的实验操作。例如，我们能够检查识别的视觉神经元如何对多个物体做出反应，这些物体可能对动物构成危险，也可能不会。通过结合多通道记录和虚拟现实技术，我们将能够研究在逼真的复杂场景呈现过程中的行为和多神经元反应。这将使我们能够确定神经系统如何检测重要的感觉线索，以及如何使用这些线索来产生适当的行为反应。结果对于理解行为的神经控制的基本原理非常重要，并且可以纳入人工系统的控制策略。

Most animals live in complex environments that often contain conflicting information about their surroundings. Survival, therefore, depends on the ability of the nervous system to collect and process relevant information, allowing the animal to generate an appropriate behavioural response. While single neurons may be suited to detect certain aspects of the environment, the combined activity of groups of neurons is crucial for detecting and deciphering complex sensory cues. Common detection schemes in animals ranging from insects to humans imply that similar fundamental processes are involved in extracting important information from the environment. Moreover, by identifying mechanisms by which groups of neurons detect relevant cues from complex stimuli, it will be possible to design better biologically-inspired artificial systems capable of adaptable, self-guided navigation. My laboratory studies responses of identified neurons in the accessible insect nervous system to relevant visual and odour cues. Specifically, we are interested in how information is processed for production of flight steering manoeuvres. Flight is a complex behaviour that requires rapid detection and processing of complex sensory signals. This behaviour is also controlled by relatively few neurons, making it accessible to rigourous experimental manipulation. For example, we are able to examine how identified visual neurons respond to multiple objects that may or may not represent a danger to the animal. By combining multichannel recording and virtual reality techniques we will be able to study behavioural and multineuronal responses during presentation of realistic complex scenes. This will allow us to identify how the nervous system detects important sensory cues and how those cues are used to generate an appropriate behavioural response. Results will be important in understanding fundamental principles of the neural control of behaviour and could be incorporated into control strategies for artificial systems.