Using deep learning to study emotional behavior in rodent models.

Autor:	Kuo JY; Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States., Denman AJ; Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States., Beacher NJ; Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States., Glanzberg JT; Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States., Zhang Y; Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States., Li Y; Department of Zoology and Physiology, University of Wyoming, Laramie, WY, United States., Lin DT; Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in behavioral neuroscience [Front Behav Neurosci] 2022 Nov 22; Vol. 16, pp. 1044492. Date of Electronic Publication: 2022 Nov 22 (Print Publication: 2022).
DOI:	10.3389/fnbeh.2022.1044492
Abstrakt:	Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Kuo, Denman, Beacher, Glanzberg, Zhang, Li and Lin.)
Databáze:	MEDLINE
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