Body language signals for rodent social communication

Ebbesen, Christian Laut; Froemke, Robert C.

doi:10.1016/j.conb.2021.01.008

Cited by 43 publications

(36 citation statements)

References 176 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies in different animal models have supported the idea that maps for actions of self and others exist in the parietal cortex of rodents as well (Mimica et al 2018;Tombaz et al 2020;Ebbesen and Froemke 2021), suggesting that such an exploitation of visual signals for action planning (Ukezono and Takano 2021) is an ancient evolutionary achievement that is widespread among mammals. Furthermore, studies in mice and bats have provided interesting evidence of the cellular mechanisms that may help agents to exploit social information to plan their behavioral responses (Kingsbury and Hong 2020), thereby directly supporting the social-affordance hypothesis.…”

Section: Discussionmentioning

confidence: 94%

Parietal maps of visual signals for bodily action planning

2021

View full text Add to dashboard Cite

The posterior parietal cortex (PPC) has long been understood as a high-level integrative station for computing motor commands for the body based on sensory (i.e., mostly tactile and visual) input from the outside world. In the last decade, accumulating evidence has shown that the parietal areas not only extract the pragmatic features of manipulable objects, but also subserve sensorimotor processing of others’ actions. A paradigmatic case is that of the anterior intraparietal area (AIP), which encodes the identity of observed manipulative actions that afford potential motor actions the observer could perform in response to them. On these bases, we propose an AIP manipulative action-based template of the general planning functions of the PPC and review existing evidence supporting the extension of this model to other PPC regions and to a wider set of actions: defensive and locomotor actions. In our model, a hallmark of PPC functioning is the processing of information about the physical and social world to encode potential bodily actions appropriate for the current context. We further extend the model to actions performed with man-made objects (e.g., tools) and artifacts, because they become integral parts of the subject’s body schema and motor repertoire. Finally, we conclude that existing evidence supports a generally conserved neural circuitry that transforms integrated sensory signals into the variety of bodily actions that primates are capable of preparing and performing to interact with their physical and social world.

show abstract

Section: Discussionmentioning

confidence: 94%

Parietal maps of visual signals for bodily action planning

2021

View full text Add to dashboard Cite

show abstract

“…The PS-VAE contributes to a growing body of research that relies on automated video analysis to facilitate scientific discovery, which often requires supervised or unsupervised dimensionality reduction approaches to first extract meaningful behavioral features from video. Notable examples include “behavioral pheno-typing,” a process which can automatically compare animal behavior across different genetic populations, disease conditions, and pharmacological interventions (Luxem et al 2020; Wiltschko, Tsukahara, et al 2020); the study of social interactions (Arac et al 2019; Zhang et al 2019; Nilsson et al 2020; Ebbesen et al 2021); and quantitative measurements of pain response (Jones et al 2020) and emotion (Dolensek et al 2020). The more detailed behavioral representation provided by the PS-VAE enables future such studies to consider a wider range of behavioral features, potentially offering a more nuanced understanding of how different behaviors are affected by genes, drugs, and the environment.…”

Section: Discussionmentioning

confidence: 99%

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Whiteway

Biderman

Friedman

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent neuroscience studies in awake and behaving animals demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from this video data. In this work we introduce a new semi-supervised framework that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this method, the Partitioned Subspace Variational Autoencoder (PS-VAE), on head-fixed mouse behavioral videos. In a close up video of a mouse face, where we track pupil location and size, our method extracts unsupervised outputs that correspond to the eyelid and whisker pad positions, with no additional user annotations required. We use this resulting interpretable behavioral representation to construct saccade and whisking detectors, and quantify the accuracy with which these signals can be decoded from neural activity in visual cortex. In a two-camera mouse video we show how our method separates movements of experimental equipment from animal behavior, and extracts unsupervised features like chest position, again with no additional user annotation needed. This allows us to construct paw and body movement detectors, and decode individual features of behavior from widefield calcium imaging data. Our results demonstrate how the interpretable partitioning of behavioral videos provided by the PS-VAE can facilitate downstream behavioral and neural analyses.

show abstract

“…The PS-VAE contributes to a growing body of research that relies on automated video analysis to facilitate scientific discovery, which often requires supervised or unsupervised dimensionality reduction approaches to first extract meaningful behavioral features from video. Notable examples include "behavioral phenotyping," a process which can automatically compare animal behavior across different genetic populations, disease conditions, and pharmacological interventions [16,55]; the study of social interactions [56][57][58][59]; and quantitative measurements of pain response [60] and emotion [61]. The more detailed behavioral representation provided by the PS-VAE enables future such studies to consider a wider range of behavioral features, potentially offering a more nuanced understanding of how different behaviors are affected by genes, drugs, and the environment.…”

Section: Discussionmentioning

confidence: 99%

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

et al. 2021

View full text Add to dashboard Cite

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

show abstract

Body language signals for rodent social communication

Cited by 43 publications

References 176 publications

Parietal maps of visual signals for bodily action planning

Parietal maps of visual signals for bodily action planning

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Contact Info

Product

Resources

About