Neuronal control of behaviour in complex sensory environments

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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. To understand the neural control of behaviour, we must first understand how an animal orients in three-dimensional space and how kinematics of locomotory appendages are driven by relevant muscle activity, which ultimately produces forces that move the animal in its environment. To understand central mechanisms of how behaviour is produced and modified, we must study how the nervous system detects environmental cues and drives appropriate locomotory muscles. While single neurons may be suited to detect specific 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. By identifying mechanisms by which groups of neurons detect relevant cues from complex stimuli, it will also be possible to design better biologically-inspired artificial systems capable of adaptable, self-guided navigation. My laboratory uses insect flight as an ideal model system to address fundamental questions about physiological mechanisms that underly natural 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 and multichannel neurophysiological recordings of single or multiple neurons involved in visual processing and 4) a virtual-reality environment to emulate real visual motion. Specifically, we study responses of identified neurons in the accessible locust nervous system to behaviourally-relevant visual cues. 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 examine how identified visual neurons respond to multiple objects or objects which change motion trajectory, which may or may not represent a danger to the animal. Past work from our group has revealed that a single, identified neuron is able to extract information about complex object motion and that other identified neurons likely contribute to higher level processing that drives natural flight behaviour. We have also found that timing of flight muscle activity predicts wing movement and animal orientation in 3-dimensional space. Using these findings, we have developed a model for avoidance behaviour that will further refine our virtual reality system and expand our current collaborations with robotics researchers. These were the first studies to show these results and drive current hypotheses that form the basis of this proposal. By combining multichannel recordings of multiple neurons and virtual reality techniques we will further understand behavioural and multineuronal responses during presentation of realistic complex scenes that this animal would encounter in its natural environment. Thus, we will contribute to an understanding of 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 will be incorporated into control strategies for artificial systems.