Using DeepLabCut for 3D markerless pose estimation across species and behaviors

Nature Protocols, Journal Year: 2019, Volume and Issue: 14(7), P. 2152 - 2176

Published: June 21, 2019

Language: Английский

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Deep learning for cellular image analysis

Nature Methods, Journal Year: 2019, Volume and Issue: 16(12), P. 1233 - 1246

Published: May 27, 2019

Language: Английский

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Single-trial neural dynamics are dominated by richly varied movements

Nature Neuroscience, Journal Year: 2019, Volume and Issue: 22(10), P. 1677 - 1686

Published: Sept. 24, 2019

Language: Английский

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Applications for deep learning in ecology

Methods in Ecology and Evolution, Journal Year: 2019, Volume and Issue: 10(10), P. 1632 - 1644

Published: July 5, 2019

Abstract A lot of hype has recently been generated around deep learning, a novel group artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course just few years, learning revolutionized several research fields such as bioinformatics and medicine with its flexibility ability process large complex datasets. As ecological datasets are becoming larger more complex, we believe these methods can be useful ecologists well. In this paper, review existing implementations show that used successfully identify species, classify animal behaviour estimate biodiversity like camera‐trap images, audio recordings videos. We demonstrate beneficial most disciplines, including applied contexts, management conservation. also common questions about how when use what steps required create network, which tools available help, requirements terms data computer power. provide guidelines, recommendations resources, reference flowchart help get started learning. argue at time automatic monitoring populations ecosystems generates vast amount cannot effectively processed by humans anymore, could become powerful tool for ecologists.

Language: Английский

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DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

eLife, Journal Year: 2019, Volume and Issue: 8

Published: Oct. 1, 2019

Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation have limitations speed robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using efficient multi-scale model, called Stacked DenseNet, fast GPU-based peak-detection algorithm estimating keypoint with subpixel precision. These improve processing >2x no loss accuracy compared methods. We demonstrate the versatility our multiple challenging tasks laboratory field settings-including groups interacting individuals. Our work reduces barriers advanced tools measuring behavior has broad applicability sciences.

Language: Английский

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SLEAP: A deep learning system for multi-animal pose tracking

Nature Methods, Journal Year: 2022, Volume and Issue: 19(4), P. 486 - 495

Published: April 1, 2022

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools quantify natural animal behavior. While advances deep learning computer vision have enabled markerless pose estimation individual animals, extending these multiple animals presents unique challenges for studies of social behaviors or their environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine system multi-animal tracking. This enables versatile workflows data labeling, model training inference on previously unseen data. SLEAP features an accessible graphical user interface, standardized model, reproducible configuration system, over 30 architectures, two approaches part grouping identity We applied seven datasets across flies, bees, mice gerbils systematically evaluate each approach architecture, compare it with other existing approaches. achieves greater accuracy speeds more than 800 frames per second, latencies less 3.5 ms at full 1,024 × image resolution. makes usable real-time applications, which demonstrate by controlling one basis tracking detection interactions another animal.

Language: Английский

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Computational Neuroethology: A Call to Action

Neuron, Journal Year: 2019, Volume and Issue: 104(1), P. 11 - 24

Published: Oct. 1, 2019

Language: Английский

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Deep learning tools for the measurement of animal behavior in neuroscience

Current Opinion in Neurobiology, Journal Year: 2019, Volume and Issue: 60, P. 1 - 11

Published: Nov. 29, 2019

Language: Английский

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Keep it real: rethinking the primacy of experimental control in cognitive neuroscience

NeuroImage, Journal Year: 2020, Volume and Issue: 222, P. 117254 - 117254

Published: Aug. 13, 2020

Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive highly-controlled experiments real-world contexts. In many cases, however, such efforts led realization that developed under particular manipulations failed capture much variance outside context manipulation. The critique non-naturalistic is not recent development; it echoes persistent and subversive thread history modern psychology. brain has evolved guide behavior multidimensional world with interacting variables. assumption artificially decoupling manipulating these variables will lead satisfactory understanding may be untenable. We develop an argument for primacy naturalistic paradigms, point developments machine learning as example transformative power relinquishing control. should deployed afterthought if hope build extend beyond laboratory into real world.

Language: Английский

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Quantifying behavior to understand the brain

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(12), P. 1537 - 1549

Published: Nov. 9, 2020

Language: Английский

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