Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 12, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

833

Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.

Yanchen Wang1, Han Yu1, Ari Blau1

  • 1Columbia University, New York, NY USA.

Arxiv
|March 11, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A multimodal approach for visualizing and identifying electrophysiological cell types in vivo.

Nature communications·2026
Same author

Exploiting correlations across trials and behavioral sessions to improve neural decoding.

Neuron·2025
Same author

Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings.

Neuron·2025
Same author

Towards robust and generalizable representations of extracellular data using contrastive learning.

Advances in neural information processing systems·2025
Same author

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms.

Neurons, behavior, data analysis, and theory·2025
Same author

A multimodal approach for visualization and identification of electrophysiological cell types <i>in vivo</i>.

bioRxiv : the preprint server for biology·2025
Same journal

When is Enough Enough? A Proposed Termination Point for the Number of Replicates in Computational Simulations.

ArXiv·2026
Same journal

A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection.

ArXiv·2026
Same journal

Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings.

ArXiv·2026
Same journal

A beam--membrane biomechanical vocal fold model incorporating posturing and glottal conformation.

ArXiv·2026
Same journal

Analyzer-less X-ray Interferometry with Super-Resolution Methods.

ArXiv·2026
Same journal

Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching.

ArXiv·2026
See all related articles

We developed BEAST (Behavioral Analysis via Self-supervised pretraining of Transformers), a new framework for analyzing animal behavior from videos. It uses unlabeled data to improve neuroscience research, even with limited labels.

Area of Science:

  • Neuroscience
  • Computer Vision
  • Machine Learning

Background:

  • Understanding the brain requires studying behavior, but current video analysis methods need extensive labeled data.
  • This limits behavioral analysis in neuroscience research, especially when labeled datasets are scarce.

Purpose of the Study:

  • To introduce BEAST (Behavioral Analysis via Self-supervised pretraining of Transformers), a scalable framework for analyzing neuro-behavioral data.
  • To leverage unlabeled video data for pretraining vision transformers for diverse behavioral analyses.

Main Methods:

  • BEAST utilizes masked autoencoding and temporal contrastive learning on unlabeled video data.
  • It pretrains experiment-specific vision transformers for behavioral analysis tasks.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Related Experiment Videos

Last Updated: Mar 12, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

833
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Main Results:

  • BEAST demonstrated improved performance across multiple species in three key tasks: behavioral feature extraction, pose estimation, and action segmentation.
  • The framework showed effectiveness in both single- and multi-animal settings.
  • BEAST accelerates behavioral analysis in data-scarce scenarios.

Conclusions:

  • BEAST provides a powerful and versatile backbone model for behavioral analysis in neuroscience.
  • The framework effectively addresses the challenge of limited labeled data in video-based behavioral studies.
  • This approach enhances the ability to correlate neural activity with specific behaviors.