We apply topological data analysis to video of C. elegans, a widely-studied URLanism in biology. We demonstrate that each of the bars in the resulting barcode may be visualized as a synthetic video of a characteristic behavior. Our main goal is to develop a quantitative summary of complex behavior that may be applied to high-throughput data. Our method is able to distinguish and classify videos from various environmental conditions . We use sliding window embeddings (time delay embeddings), persistent homology, average persistence landscapes, and support vector machines .
We apply topological data analysis to the behavior of C. elegans, a widely-studied URLanism in biology. In particular, we use topology to produce a quantitative summary of complex behavior which may be applied to high-throughput data. Our methods allow us to distinguish and classify videos from various environmental conditions and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors .
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We demonstrate that each of the bars in the resulting barcode may be visualized as a synthetic video of a characteristic behavior. Our main goal is to develop
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. We use sliding window embeddings (time delay embeddings), persistent homology, average persistence landscapes, and support vector machines
and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors