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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Temporal orchestration of transcriptional and epigenomic programming underlying maternal embryonic diapause in a cricket model.

Communications biology·2026
Same author

Development of HAUP-based method for measuring simultaneously linear birefringence and dichroism near diagonal position in anisotropic media.

Scientific reports·2026
Same author

A hetero-chitooligosaccharide as plant defense elicitors against spider mites.

Pest management science·2026
Same author

Identification of Paramyosin as a Novel Allergen in Edible Cricket Protein.

Journal of nutritional science and vitaminology·2026
Same author

Chromosome-scale genome assembly and annotation of the two-spotted cricket Gryllus bimaculatus (Orthoptera: Gryllidae).

G3 (Bethesda, Md.)·2026
Same author

Gut Microbiome Differences Across Mixed-Sex and Female-Only Social Rearing Regimes in Female Field Crickets <i>Teleogryllus occipitalis</i> (Orthoptera: Gryllidae).

Insects·2026

Related Experiment Video

Updated: Jun 29, 2025

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K

DeepLabCut-based daily behavioural and posture analysis in a cricket.

Shota Hayakawa1, Kosuke Kataoka2, Masanobu Yamamoto3

  • 1Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 162-8480, Japan.

Biology Open
|March 27, 2024
PubMed
Summary

This study introduces a novel system for tracking cricket behavior, including feeding and sleep, using deep learning. This advancement offers a more comprehensive understanding of circadian rhythms in insects.

Keywords:
Circadian rhythmCricketMachine learningPosture analysisSleep-like state

More Related Videos

Studying the Neural Basis of Adaptive Locomotor Behavior in Insects
10:19

Studying the Neural Basis of Adaptive Locomotor Behavior in Insects

Published on: April 13, 2011

12.9K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.5K

Related Experiment Videos

Last Updated: Jun 29, 2025

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K
Studying the Neural Basis of Adaptive Locomotor Behavior in Insects
10:19

Studying the Neural Basis of Adaptive Locomotor Behavior in Insects

Published on: April 13, 2011

12.9K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.5K

Area of Science:

  • Animal behavior research
  • Chronobiology
  • Machine learning applications in biology

Background:

  • Circadian rhythms govern daily physiological cycles like feeding and sleep.
  • Crickets are established models for insect circadian rhythm research.
  • Previous methods for measuring cricket behavior were limited to locomotion analysis.

Purpose of the Study:

  • To develop a novel system for simultaneous, long-term quantification of multiple cricket behaviors.
  • To leverage deep learning for unbiased, non-invasive animal behavior analysis.
  • To enable a more detailed study of circadian rhythms in insects.

Main Methods:

  • Utilized DeepLabCut, a supervised machine learning software, for body keypoint labeling.
  • Developed a system to simultaneously track locomotor activity, feeding, and sleep-like states in individual crickets.
  • Focused on posture analysis for sleep-like state estimation, moving beyond traditional immobility metrics.

Main Results:

  • The system accurately labeled six body parts of individual crickets with high confidence.
  • Reliable data on diurnal rhythms of multiple behaviors were generated.
  • Enabled estimation of sleep-like states based on posture, offering a new metric.

Conclusions:

  • The developed system provides a powerful tool for simultaneous and automatic prediction of cricket behavior and posture.
  • This approach facilitates a deeper understanding of the neural mechanisms underlying circadian rhythms.
  • The system has the potential to advance insect chronobiology research significantly.