Like many other animals, humans use visual cues to navigate through the environment. Our own movements through the surround generate wide-field optic flow across our retina that we use to ensure that we stay on a straight path, and avoid obstacles. Besides this type of motion, we also need to be able to discriminate objects that move relative to the remaining visual surround. For example, if you are playing tennis you can see the ball while you are running yourself, even though this scenario means that you have to disambiguate the ball’s motion from the self-generated optic flow. Such target detection in visual clutter is a challenging task by any system, man-made or natural. While artificial vision systems struggle to solve this task in real-time, despite using high-performing computers and top-end cameras, evolution has beautifully solved it even in the tiniest of insects, as well as in vertebrates such as us humans. The sophisticated flight behavior of insects during conspecific interactions and prey capture is evidence of this. This feat becomes even more impressive when considering that insects do this with a tiny brain and an eye with really poor resolution compared to even an old mobile phone camera. As opposed to humans, insects are accessible for in vivo electrophysiological recordings, and thus provide an excellent model system for investigating the mechanisms of motion vision, and in particular of target detection.
In the Hoverfly Vision group we use hoverflies to understand how the nervous system codes visual information. We use a range of techniques, such as electrophysiology of single neurons in the fly brain and the descending nerve cord, quantitative behavior, free flight experiments, and field site measurements. Our naturalistic approach allows us to use behaviorally relevant stimuli in more controlled laboratory experiments.
For more information: https://hoverflyvision.weebly.com/