A leg to stand on: computational models of proprioception

Dallmann, Chris J.; Karashchuk, Pierre; Brunton, Bingni W.; Tuthill, John C.

doi:10.1016/j.cophys.2021.03.001

Cited by 18 publications

(16 citation statements)

References 84 publications

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“…Which proprioceptors, if any, are used to sense femur and coxa rotation of the middle legs? Answering these questions will be facilitated by combining Anipose with in vivo measurements and perturbations of proprioceptive neural circuits (Dallmann et al, 2021).…”

Section: Impact Of Robust Markerless 3d Trackingmentioning

confidence: 99%

“…For tracking multiple animals in 3D, a promising approach is to build correspondences based on geometry and appearance (Dong et al, 2019) across multiple views. As automated, highthroughput tracking of animal behavior grows in scale, new methods for data analysis, visualization, and modeling will also be needed to gain insight into the neural control of dynamic behavior (York et al, 2020;Marshall et al, 2021;Berman et al, 2014;Dallmann et al, 2021).…”

Section: Potential For Future Improvement Based On Related Workmentioning

confidence: 99%

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Anipose: A toolkit for robust markerless 3D pose estimation

et al. 2021

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Section: Impact Of Robust Markerless 3d Trackingmentioning

confidence: 99%

Section: Potential For Future Improvement Based On Related Workmentioning

confidence: 99%

Anipose: A toolkit for robust markerless 3D pose estimation

et al. 2021

Self Cite

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show abstract

Section: Impact Of Robust Markerless 3d Trackingmentioning

confidence: 99%

“…For tracking multiple animals in 3D, a promising approach is to build correspondences based on geometry and appearance [80] across multiple views. As automated, high-throughput tracking of animal behavior grows in scale, new methods for data analysis, visualization, and modeling will also be needed to gain insight into the neural control of dynamic behavior [81,10,45,58].…”

Section: Potential For Future Improvement Based On Related Workmentioning

confidence: 99%

Anipose: a toolkit for robust markerless 3D pose estimation

Karashchuk

Rupp

Dickinson

et al. 2020

Preprint

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Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals live and move in 3D. Here, we introduce Anipose, a Python toolkit for robust markerless 3D pose estimation. Anipose consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial constraints, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on four datasets: a moving calibration board, fruit flies walking on a treadmill, mice reaching for a pellet, and humans performing various actions. Because Anipose is built on popular 2D tracking methods (e.g., DeepLabCut), users can expand their existing experimental setups to incorporate robust 3D tracking. We hope this opensource software and accompanying tutorials (anipose.org) will facilitate the analysis of 3D animal behavior and the biology that underlies it.

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“…These motor behaviors result from complex interactions between the neural circuits in the brain and spinal cord, the musculoskeletal system, and the environment ( Fig. 1 ) [ 1 ]–[ 5 ]. Investigating the underlying mechanisms of these motor behaviors in awake, behaving animals is highly challenging.…”

Section: Introductionmentioning

confidence: 99%

A Whole-Body Musculoskeletal Model of the Mouse

et al. 2021

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Neural control of movement cannot be fully understood without careful consideration of interactions between the neural and biomechanical components. Recent advancements in mouse molecular genetics allow for the identification and manipulation of constituent elements underlying the neural control of movement. To complement experimental studies and investigate the mechanisms by which the neural circuitry interacts with the body and the environment, computational models of motor behaviors in mice need to incorporate the corresponding model of the mouse musculoskeletal system. Here, we present the first fully articulated musculoskeletal model of the mouse. The mouse skeletal system has been developed from anatomical references and includes the sets of bones in all body compartments, including four limbs, spine, head and tail. Joints between all bones allow for simulation of full 3D mouse kinematics and kinetics. Hindlimb and forelimb musculature has been implemented using Hill-type muscle models. We analyzed the mouse whole-body model and described the moment-arms for different hindlimb and forelimb muscles, the moments applied by these muscles on the joints, and their involvement in limb movements at different limb/body configurations. The model represents a necessary step for the subsequent development of a comprehensive neuro-biomechanical model of freely behaving mice; this will close the loop between the neural control and the physical interactions between the body and the environment.

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A leg to stand on: computational models of proprioception

Cited by 18 publications

References 84 publications

Anipose: A toolkit for robust markerless 3D pose estimation

Anipose: A toolkit for robust markerless 3D pose estimation

Anipose: a toolkit for robust markerless 3D pose estimation

A Whole-Body Musculoskeletal Model of the Mouse

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