PT - JOURNAL ARTICLE AU - Julia C. Costacurta AU - Lea Duncker AU - Blue Sheffer AU - Winthrop Gillis AU - Caleb Weinreb AU - Jeffrey E. Markowitz AU - Sandeep R. Datta AU - Alex H. Williams AU - Scott W. Linderman TI - Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs AID - 10.1101/2022.06.10.495690 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.06.10.495690 4099 - http://biorxiv.org/content/early/2022/06/28/2022.06.10.495690.short 4100 - http://biorxiv.org/content/early/2022/06/28/2022.06.10.495690.full AB - A core goal in systems neuroscience and neuroethology is to understand how neural circuits generate naturalistic behavior. One foundational idea is that complex naturalistic behavior may be composed of sequences of stereotyped behavioral syllables, which combine to generate rich sequences of actions. To investigate this, a common approach is to use autoregressive hidden Markov models (ARHMMs) to segment video into discrete behavioral syllables. While these approaches have been successful in extracting syllables that are interpretable, they fail to account for other forms of behavioral variability, such as differences in speed, which may be better described as continuous in nature. To overcome these limitations, we introduce a class of warped ARHMMs (WARHMM). As is the case in the ARHMM, behavior is modeled as a mixture of autoregressive dynamics. However, the dynamics under each discrete latent state (i.e. each behavioral syllable) are additionally modulated by a continuous latent “warping variable.” We present two versions of warped ARHMM in which the warping variable affects the dynamics of each syllable either linearly or nonlinearly. Using depth-camera recordings of freely moving mice, we demonstrate that the failure of ARHMMs to account for continuous behavioral variability results in duplicate cluster assignments. WARHMM achieves similar performance to the standard ARHMM while using fewer behavioral syllables. Further analysis of behavioral measurements in mice demonstrates that WARHMM identifies structure relating to response vigor.Competing Interest StatementThe authors have declared no competing interest.