Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs.

Autor: Mimura K; Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan. mimura.koki@qst.go.jp.; Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, 190-0014, Japan. mimura.koki@qst.go.jp., Matsumoto J; Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, 930-8555, Japan.; Research Center for Idling Brain Science, University of Toyama, Toyama, 930-8555, Japan., Mochihashi D; Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics, Tokyo, 190-9562, Japan., Nakamura T; Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Tokyo, 182-8585, Japan., Nishijo H; Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, 930-8555, Japan.; Research Center for Idling Brain Science, University of Toyama, Toyama, 930-8555, Japan., Higuchi M; Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan., Hirabayashi T; Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan., Minamimoto T; Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan. minamimoto.takafumi@qst.go.jp.
Jazyk: angličtina
Zdroj: Communications biology [Commun Biol] 2024 Sep 03; Vol. 7 (1), pp. 1080. Date of Electronic Publication: 2024 Sep 03.
DOI: 10.1038/s42003-024-06786-2
Abstrakt: Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common marmosets and rhesus monkeys, SMP outperformed conventional posture-clustering models and detected a set of behavioral ethograms from publicly available data. SMP also quantified and visualized the behavioral effects of chemogenetic neural manipulations. SMP thus has the potential to dramatically improve our understanding of natural NHP behavior in a variety of contexts.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje