  We apply topological data analysis to the behavior of C. elegans, a
widely-studied model organism 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.
