<ul><li>Researchers have introduced a new algorithm for finding robust circular coordinates on recurrent time series data, such as neuronal recordings of C. elegans.</li><li>The algorithm corrects for uneven sampling density by adapting the method of averaging coordinates in manifold learning.</li><li>Rejection sampling is used to address inhomogeneous sampling, and Procrustes matching is applied to align and average the subsamples.</li><li>The technique is validated on synthetic data sets and neuronal activity recordings, revealing a topological model for C. elegans' neuronal trajectories.</li></ul>

Subsampling, aligning, and averaging to find circular coordinates in recurrent time series

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