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Showing 1–7 of 7 results
Advanced filters: Author: Mackenzie W. Mathis Clear advanced filters

  • Quantifying animal behavior is crucial in various fields such as neuroscience and ecology, yet we lack data-efficient methods to perform behavioral quantification. Here, the authors provide new unified models across 45+ species without manual labeling, thus enhancing analysis in behavioral studies.

    • Shaokai Ye
    • Anastasiia Filippova
    • Mackenzie Weygandt Mathis
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-19

  • 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. This allows noninvasive behavioral tracking of movement.

    • Tanmay Nath
    • Alexander Mathis
    • Mackenzie Weygandt Mathis
    Protocols
    Nature Protocols
    Volume: 14, P: 2152-2176

  • Keypoint-MoSeq is an unsupervised behavior segmentation algorithm that extracts behavioral modules from keypoint tracking data acquired with diverse algorithms, as demonstrated in mice, rats and fruit flies. The extracted modules faithfully reflect human-annotated behaviors even though they are obtained in an unsupervised fashion.

    • Caleb Weinreb
    • Jonah E. Pearl
    • Sandeep Robert Datta
    ResearchOpen Access
    Nature Methods
    Volume: 21, P: 1329-1339

  • Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.

    • Alexander Mathis
    • Pranav Mamidanna
    • Matthias Bethge
    Research
    Nature Neuroscience
    Volume: 21, P: 1281-1289

  • A new encoding method, CEBRA, jointly uses behavioural and neural data in a (supervised) hypothesis- or (self-supervised) discovery-driven manner to produce both consistent and high-performance latent spaces.

    • Steffen Schneider
    • Jin Hwa Lee
    • Mackenzie Weygandt Mathis
    ResearchOpen Access
    Nature
    Volume: 617, P: 360-368

  • Animal ecologists are increasingly limited by constraints in data processing. Here, Tuia and colleagues discuss how collaboration between ecologists and data scientists can harness machine learning to capitalize on the data generated from technological advances and lead to novel modeling approaches.

    • Devis Tuia
    • Benjamin Kellenberger
    • Tanya Berger-Wolf
    ReviewsOpen Access
    Nature Communications
    Volume: 13, P: 1-15