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We are interested in how networks of neurons perform computations. In order to dissect a neural computation, one must understand the task being performed, control the network’s inputs, and manipulate the system’s component parts. The fruit fly’s visual-motor transformations serve as an ideal model in which we can hope to follow neural processing as the brain transforms a visual stimulus into a behavioral output. In order to uncover principles and mechanisms of visual processing, we create novel stimuli, measure fly behavior, monitor neural activity, and employ genetics to manipulate circuit function. In particular, we want to understand how animals extract motion information from the complex spatio-temporal visual patterns in the natural world, and how they make decisions based on that information. We want to understand and model how the system computes at an algorithmic level, and also to use the battery of genetic tools in the fruit fly to discover the neural and biophysical mechanisms that implement those algorithms. By comparing fly algorithms and mechanisms to vertebrate ones, we look for evolutionary constraints and diversity in solutions to basic computational tasks.

If you are an undergraduate student, graduate student, or prospective postdoc with a background in neuroscience, biophysics, engineering, or physics, and you are interested in joining our team, please contact Damon Clark.