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Damon Clark studied physics as an undergraduate at Princeton, where he became interested in neuroscience. He received his PhD in physics in 2007 from Harvard, where he studied navigational behavior and sensory processing in the roundworm C. elegans, a model system for neuroscience. After a postdoc in neurobiology at Stanford, he joined the faculty at Yale in 2013. His lab investigates the algorithms and neural underpinnings of visual processing in flies, with the twin goals of understanding (1) how individual neurons contribute to circuit computations and (2) how these computations are shared across animals and sensory modalities.

What do you do with Data Science?

We apply machine learning to understand the structure of visual scenes and the circuits that process those scenes to extract information. For instance, we have applied machine learning to understand how specific types of spatiotemporal correlations may be used to estimate local motion (Fitzgerald & Clark, 2015). We have been especially interested in training neural networks to perform the same tasks as biological networks, while applying anatomical constraints on the artificial neural network (ANN). This allows us to map nodes in the ANNs to individual neurons in an animal’s visual system – the fly, in this case. This sort of anatomically constrained task optimization has taught us about the organization of the fly’s motion detection circuits and its loom sensing circuits (Mano et al., 2021; Zhou et al., 2022 (with John Lafferty)). We are currently working on an information bottleneck approach to understanding why intermediate motion signals in the fly visual system are structured as they are.

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