ReviewFrontiers in behavioral neuroscience2022
Using deep learning to study emotional behavior in rodent models.
Review in Frontiers in behavioral neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Research on improved models for facial expression recognition in mice with abnormal glucose metabolism.Scientific reports · 2026Article
- Profiling mouse behavior with computational tools to assess age-dependent differences in associative learning.Cell reports methods · 2025Article
- Using miniscopes and deep learning to compare neurobehavioral representations of psychostimulant and opioid self-administration.Addiction neuroscience · 2025Article
- Miniscope Imaging of Nucleus Accumbens Neural Activity in Freely Behaving Rats: Virus Injection, Gradient Index Lens Implantation, Recording Strategies, and Analytical Methods.Current protocols · 2025Article
- Article
- Minibox: Custom solo or semi-group housing chambers for long term housing of rats with miniscopes.MethodsX · 2024Article
- Article
- Examining a punishment-related brain circuit with miniature fluorescence microscopes and deep learning.Addiction neuroscience · 2024Article
- Data management strategy for a collaborative research center.GigaScience · 2022Article
- The Effect of Earthing Mat on Stress-Induced Anxiety-like Behavior and Neuroendocrine Changes in the Rat.Biomedicines · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.