Convolutional Neural Network Analysis of Social Novelty Preference using DeepLabCut

Worley, Nicholas B.; Djerdjaj, Anthony; Christianson, John P.

doi:10.1101/736983

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…With that pretraining in place, DLC only needs a few training examples (typically 50 -200 frames) to achieve human-level accuracy, making it a highly data-efficient software 16,21 . DLC has already been implemented in different research fields including neuroscience [22][23][24] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning based behavioral profiling of rodent stroke recovery

Weber

Mulders

Kaiser

et al. 2021

Preprint

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Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reproducible, and often unsuitable for unraveling the complex behavior after injury. Here, we provide a comprehensive 3D gait analysis of mice after focal cerebral ischemia based on the new deep learning-based software (DeepLabCut, DLC) that only requires basic behavioral equipment. We demonstrate a high precision 3D tracking of 10 body parts (including all relevant joints and reference landmarks) in several mouse strains with an accuracy of 99.4%. Building on this rigor motion tracking, a comprehensive post-analysis (with >100 parameters) unveils biologically relevant differences in locomotor profiles after a stroke over a time course of three weeks. We further refine the widely used ladder rung test using deep learning and compare its performance to human annotators. The generated DLC-assisted tests were then benchmarked to five widely used conventional behavioral set-ups (neurological scoring, rotarod, ladder rung walk, cylinder test, and single-pellet grasping) regarding sensitivity, accuracy, time use and costs. We conclude that deep learning-based motion tracking with comprehensive post-analysis provides accurate and sensitive data to describe the complex recovery of rodents following a stroke. The experimental set-up and analysis can also benefit a range of other neurological injuries that affect locomotion.

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Section: Introductionmentioning

confidence: 99%

Deep learning based behavioral profiling of rodent stroke recovery

Weber

Mulders

Kaiser

et al. 2021

Preprint

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“…After recording the chicks’ movement inside the cage, the videos can be instead automatically analysed by an artificial neural network, extracting the position of different body parts for each frame. We routinely performed this with DeepLabCut (Mathis et al 2018 ; Nath et al 2019 ), a powerful tool largely used in computational ethology studies (Labuguen et al 2021 ; Worley et al 2019 ; Wu et al 2020 ). After positions’ extraction, a CSV file with all the body parts coordinates is available, from which a whole statistical analysis is coded.…”

Section: Complete Workflowmentioning

confidence: 99%

Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions

2021

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Soon after hatching, the young of precocial species, such as domestic chicks or ducklings, learn to recognize their social partner by simply being exposed to it (imprinting process). Even artificial objects or stimuli displayed on monitor screens can effectively trigger filial imprinting, though learning is canalized by spontaneous preferences for animacy signals, such as certain kinds of motion or a face-like appearance. Imprinting is used as a behavioural paradigm for studies on memory formation, early learning and predispositions, as well as number and space cognition, and brain asymmetries. Here, we present an automatized setup to expose and/or test animals for a variety of imprinting experiments. The setup consists of a cage with two high-frequency screens at the opposite ends where stimuli are shown. Provided with a camera covering the whole space of the cage, the behaviour of the animal is recorded continuously. A graphic user interface implemented in Matlab allows a custom configuration of the experimental protocol, that together with Psychtoolbox drives the presentation of images on the screens, with accurate time scheduling and a highly precise framerate. The setup can be implemented into a complete workflow to analyse behaviour in a fully automatized way by combining Matlab (and Psychtoolbox) to control the monitor screens and stimuli, DeepLabCut to track animals’ behaviour, Python (and R) to extract data and perform statistical analyses. The automated setup allows neuro-behavioural scientists to perform standardized protocols during their experiments, with faster data collection and analyses, and reproducible results.

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“…The open-source toolbox DeepLabCut copes with these limitations (Mathis et al 2018;Nath et al 2019). DeepLabCut exploits deep learning techniques to track animals' movements with unprecedented accuracy, without the need to apply any marker on the body of the animal (Labuguen et al 2019;Wu et al 2019;Worley et al 2019), opening a new range of possibilities for measuring animal behaviour such as preferential eyeuse.…”

Section: Introductionmentioning

confidence: 99%

Visual Field Analysis: a reliable method to score left- and right eye-use using automated tracking

Josserand

Rosa‐Salva

Versace

et al. 2021

Preprint

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Brain and behavioural asymmetries have been documented in various taxa. Many of these asymmetries involve preferential left- and right-eye use. However, measuring eye use through manual frame-by-frame analyses from video recordings is laborious and may lead to biases. Recent progress in technology allowed the development of accurate tracking techniques for measuring animal behaviour. Amongst these techniques, DeepLabCut, a python-based tracking toolbox using transfer learning with deep neural networks, offers the possibility to track different body parts with unprecedented accuracy. Exploiting the potentialities of DeepLabCut, we developed 'Visual Field Analysis', an additional open-source application for extracting eye-use data. To our knowledge, this is the first application that can automatically quantify left-right preferences in eye use. Here we test the performance of our application in measuring preferential eye-use in young domestic chicks. The comparison with manual scoring methods revealed a perfect correlation in the measures of eye-use obtained by 'Visual Field Analysis'. With our application, eye-use can be analysed reliably, objectively and at a fine scale in different experimental paradigms.

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Convolutional Neural Network Analysis of Social Novelty Preference using DeepLabCut

Cited by 6 publications

References 12 publications

Deep learning based behavioral profiling of rodent stroke recovery

Deep learning based behavioral profiling of rodent stroke recovery

Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions

Visual Field Analysis: a reliable method to score left- and right eye-use using automated tracking

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