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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Perspectives in machine learning for wildlife conservation

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Feb. 9, 2022

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders bio-logging devices. These new technologies the data they generate hold great potential for large-scale environmental monitoring understanding, but are limited by current processing approaches which inefficient how ingest, digest, distill into relevant information. We argue that machine learning, especially deep learning approaches, can meet this analytic challenge enhance our capacity, conservation of wildlife species. Incorporating ecological workflows could improve inputs population behavior models eventually lead integrated hybrid modeling tools, with acting constraints latter providing data-supported insights. In essence, combining domain knowledge, ecologists capitalize on abundance generated modern sensor order reliably estimate abundances, study mitigate human/wildlife conflicts. To succeed, approach will require close collaboration cross-disciplinary education between computer science communities ensure quality train a generation scientists conservation.

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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Thermal constraints on in vivo optogenetic manipulations

Nature Neuroscience, Journal Year: 2019, Volume and Issue: 22(7), P. 1061 - 1065

Published: June 17, 2019

Language: Английский

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Multi-animal pose estimation, identification and tracking with DeepLabCut

Nature Methods, Journal Year: 2022, Volume and Issue: 19(4), P. 496 - 504

Published: April 1, 2022

Abstract Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate association detected keypoints to correct individuals, as well having highly similar looking that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source estimation toolbox, provide high-performance animal assembly tracking—features required for multi-animal Furthermore, integrate ability predict animal’s identity assist tracking (in case occlusions). We illustrate power framework with four datasets varying complexity, which release serve benchmark future algorithm development.

Language: Английский

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Oxytocin, Neural Plasticity, and Social Behavior

Annual Review of Neuroscience, Journal Year: 2021, Volume and Issue: 44(1), P. 359 - 381

Published: April 7, 2021

Oxytocin regulates parturition, lactation, parental nurturing, and many other social behaviors in both sexes. The circuit mechanisms by which oxytocin modulates behavior are receiving increasing attention. Here, we review recent studies on modulation of neural function behavior, largely enabled new methods monitoring manipulating or receptor neurons vivo. These indicate that can enhance the salience stimuli increase signal-to-noise ratios modulating spiking synaptic plasticity context circuits networks. We highlight effects nontraditional organisms such as prairie voles discuss opportunities to utility these for studying circuit-level behaviors. then insights into neuron activity during interactions. conclude discussing some major questions field ahead.

Language: Английский

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Artificial Neural Networks for Neuroscientists: A Primer

Neuron, Journal Year: 2020, Volume and Issue: 107(6), P. 1048 - 1070

Published: Sept. 1, 2020

Language: Английский

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Cortical pattern generation during dexterous movement is input-driven

Nature, Journal Year: 2019, Volume and Issue: 577(7790), P. 386 - 391

Published: Dec. 25, 2019

Language: Английский

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