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Poster
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
Julia Costacurta · Lea Duncker · Blue Sheffer · Winthrop Gillis · Caleb Weinreb · Jeffrey Markowitz · Sandeep R Datta · Alex Williams · Scott Linderman

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #511
in Poster Session 5 »

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.

Keywords: Clustering · naturalistic behavior · markov models · time series
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Julia Costacurta (Stanford University)
Lea Duncker (Stanford University)
Blue Sheffer (Stanford University)
Winthrop Gillis (Harvard Medical School)
Caleb Weinreb (Harvard University)
Jeffrey Markowitz (Georgia Institute of Technology)
Sandeep R Datta (Harvard Medical School)
Alex Williams (New York University)
Scott Linderman (Stanford University)

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