| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-09-05 |
| タイトル |
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タイトル |
Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs. |
|
言語 |
en |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Koki Mimura
Jumpei Matsumoto
Daichi Mochihashi
Tomoaki Nakamura
Hisao Nishijo
Makoto Higuchi
Toshiyuki Hirabayashi
Takafumi Minamimoto
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
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. |
| 書誌情報 |
Communications biology
巻 7,
号 1,
p. 1080,
発行日 2024-09
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2399-3642 |
| PubMed番号 |
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識別子タイプ |
PMID |
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関連識別子 |
39227400 |
| DOI |
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|
識別子タイプ |
DOI |
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関連識別子 |
10.1038/s42003-024-06786-2 |
