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JAABA: interactive machine learning for automatic annotation of animal behavior

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TLDR
This work presents a machine learning–based system that can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.
Abstract
Open-source software that allows biologists to create a variety of behavior classifiers for automatically annotating video of behaving animals is presented. The program, called JAABA, uses state-of-the-art machine-learning methods and is applicable to tracking data from different organisms, including mice and adult and larval Drosophila.

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

TL;DR: This protocol describes how to use an open-source toolbox, DeepLabCut, to train a deep neural network to precisely track user-defined features with limited training data, which allows noninvasive behavioral tracking of movement.
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Mapping Sub-Second Structure in Mouse Behavior.

TL;DR: Depth imaging is used to show that 3D mouse pose dynamics are structured at the sub-second timescale and reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.
Journal ArticleDOI

Mapping the stereotyped behaviour of freely moving fruit flies

TL;DR: In this paper, a method for mapping an animal's actions, relying only upon the underlying structure of postural movement data to organize and classify behaviours, was introduced, and applied to the ground-based behaviour of the fruit fly, Drosophila melanogaster.
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Automated image-based tracking and its application in ecology

TL;DR: Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.
References
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Journal ArticleDOI

Additive Logistic Regression : A Statistical View of Boosting

TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.
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Human activity analysis: A review

TL;DR: This article provides a detailed overview of various state-of-the-art research papers on human activity recognition, discussing both the methodologies developed for simple human actions and those for high-level activities.
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Tools for neuroanatomy and neurogenetics in Drosophila

TL;DR: The results suggest that the D. melanogaster genome contains >50,000 enhancers and that multiple enhancers drive distinct subsets of expression of a gene in each tissue and developmental stage.
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High-throughput Ethomics in Large Groups of Drosophila

TL;DR: A camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena finds that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype.
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