Multiscale dynamics of rat locomotion

To effectively connect animal behaviors to activities and patterns in the nervous system, it is ideal have a precise, accurate, and complete description of stereotyped modules and their dynamics in behaviors. In case of rodent behaviors, observers have identified and described several stereotyped behaviors, such as grooming and lateral threat. Discovering behavioral repertoires in this way is imprecise, slow and contaminated with biases and individual differences. As a replacement, we propose a framework for unbiased, efficient and precise investigation of rat locomotor activities. We propose that locomotion possesses multiscale dynamics that can be well approximated by multiple Markov processes running in parallel at different spatial-temporal scales. To capture motifs and transition dynamics on multiple scales, we developed a segmentation-decomposition procedure, which imposes explicit constraints on timescales on parallel Hidden Markov Models (HMM). Each HMM describes the motifs and transition dynamics at its respective timescale. We showed that the motifs discovered across timescales have experimental significance and space-dependent heterogeneity. Through statistical tests, we show that locomotor dynamics largely conforms with Markov property across scales. Finally, using layered HMMs, we showed that motif assembly is strongly constrained to a few fixed sequences. The motifs potentially reflect outputs of canonical underlying behavioral output motifs. Our approach and results for the first time capture behavioral dynamics at different spatial-temporal scales, painting a more complete picture of how behaviors are organized.