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Review
. 2022 Nov 22:16:1044492.
doi: 10.3389/fnbeh.2022.1044492. eCollection 2022.

Using deep learning to study emotional behavior in rodent models

Jessica Y Kuo  1 , Alexander J Denman  1 , Nicholas J Beacher  1 , Joseph T Glanzberg  1 , Yan Zhang  1 , Yun Li  2 , Da-Ting Lin  1
Affiliations
Review

Using deep learning to study emotional behavior in rodent models

Jessica Y Kuo et al. Front Behav Neurosci. .

Abstract

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.

Keywords: deep learning; emotion; neural recording; pose estimation; self-supervised learning; supervised learning; unsupervised learning.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Schematic diagram of supervised, unsupervised, and self-supervised approaches for animal behavior classification. (A) In supervised approaches, a model is trained to generate behavioral classifiers that replicate human annotations. (B) Unsupervised approaches are entirely data driven; pose estimation data is compressed to the latent state representation and clustered to maximize similarities in data points. Experimenters may sometimes specify the number of desired clusters. (C) In self-supervised approaches, a model is trained to generate labels derived from the data. The trained model is used to map subject behavior to a latent space, and the space is then discretized (usually with K-means).
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