Papers › Quantifying the behavioral dynamics of C. elegans with autoregressive hidden Markov models
Quantifying the behavioral dynamics of C. elegans with autoregressive hidden Markov models
E. Kelly Buchanan, Akiva Lipshitz, Scott Linderman, Liam Paninski
In order to fully understand the neural activity of Caenorhabditis elegans, we need a rich, quantitative description of the behavioral outputs it gives rise to. To this end, we quantify the behavioral dynamics of the worm with autoregressive hidden Markov models (AR-HMMs), a class of models that has recently yielded some insight into mouse behavior [1]. These models explicitly encode three hypotheses: (i) while the instantaneous posture of the worm is represented as a high-dimensional vector of points along the body, the first four principal components, or eigenworms, capture a significant fraction of the postural variance; (ii) within this four dimensional space, the postural dynamics are well-approximated with linear autoregressive models; and (iii) the linear autoregressive model switches over time as the worm transitions between different discrete behaviors, like forward crawling, reverse crawling, pausing, and turning. We show how AR-HMMs segment recordings of freely crawling C. elegans into meaningful discrete behaviors, providing a quantitative description of postural dynamics and a rigorous framework for assessing, comparing, and simulating worm behavior.
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