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Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs

Julia C. Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman
doi: https://doi.org/10.1101/2022.06.10.495690
Julia C. Costacurta
1Wu Tsai Neurosciences Institute, Stanford, CA, USA
2Department of Electrical Engineering, Stanford, CA, USA
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  • For correspondence: jcostac@stanford.edu scott.linderman@stanford.edu
Lea Duncker
1Wu Tsai Neurosciences Institute, Stanford, CA, USA
3Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
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Blue Sheffer
1Wu Tsai Neurosciences Institute, Stanford, CA, USA
4Department of Computer Science, Stanford University, Stanford, CA, USA
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Winthrop Gillis
5Program in Neuroscience, Harvard University, Boston, MA
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Caleb Weinreb
6Department of Neurobiology, Harvard University, Boston, MA
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Jeffrey E. Markowitz
7Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Sandeep R. Datta
6Department of Neurobiology, Harvard University, Boston, MA
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Alex H. Williams
8Center for Neural Science, New York University, New York City, NY, USA
9Center for Computational Neuroscience, Flatiron Institute, New York City, NY, USA
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Scott W. Linderman
1Wu Tsai Neurosciences Institute, Stanford, CA, USA
10Department of Statistics, Stanford, CA, USA
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  • For correspondence: jcostac@stanford.edu scott.linderman@stanford.edu
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Abstract

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 Statement

The authors have declared no competing interest.

Footnotes

  • Updated author list; added author contributions

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 28, 2022.
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Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
Julia C. Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman
bioRxiv 2022.06.10.495690; doi: https://doi.org/10.1101/2022.06.10.495690
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Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
Julia C. Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman
bioRxiv 2022.06.10.495690; doi: https://doi.org/10.1101/2022.06.10.495690

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