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

Whole-body 3D kinematics of freely behaving <i>Drosophila</i>.

bioRxiv : the preprint server for biology·2026
Same author

Binding as an enabling mechanism for cross-modal plasticity.

Neuroscience and biobehavioral reviews·2026
Same author

Thalamic activation of the visual cortex at the single-synapse level.

Science (New York, N.Y.)·2026
Same author

Morphoelectric properties of inhibitory neurons shift gradually and regardless of cell type along the depth of the cerebral cortex.

bioRxiv : the preprint server for biology·2026
Same author

Neuronal identity is not static: An input-driven perspective.

PLoS computational biology·2025
Same author

NeuroCarta: An automated and quantitative approach to mapping cellular networks in the mouse brain.

Network neuroscience (Cambridge, Mass.)·2025

Related Experiment Video

Updated: Jul 5, 2026

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice
10:14

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

Unsupervised whisker tracking in unrestrained behaving animals.

Jakob Voigts1, Bert Sakmann, Tansu Celikel

  • 1Undergraduate Program in Mathematics, University of Heidelberg, Heidelberg, Germany.

Journal of Neurophysiology
|May 9, 2008
PubMed
Summary

Researchers developed an unsupervised algorithm to track whisker movements and quantify tactile information during haptic exploration in mice. This method enables detailed analysis of whisking kinematics and whisker touch, advancing our understanding of sensory processing.

More Related Videos

Non-aversive Animal Restraint Enabling Recording of Optomotor Reflex in Ground Squirrels
07:28

Non-aversive Animal Restraint Enabling Recording of Optomotor Reflex in Ground Squirrels

Published on: July 25, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

Related Experiment Videos

Last Updated: Jul 5, 2026

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice
10:14

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

Non-aversive Animal Restraint Enabling Recording of Optomotor Reflex in Ground Squirrels
07:28

Non-aversive Animal Restraint Enabling Recording of Optomotor Reflex in Ground Squirrels

Published on: July 25, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

Area of Science:

  • Neuroscience
  • Biophysics
  • Robotics

Background:

  • Quantifying whisker-based tactile sensory input is crucial for understanding neural representations during whisking and touch.
  • Existing electrophysiological methods lack robust techniques for measuring whisker sensory input in behaving animals.

Purpose of the Study:

  • To develop and validate an unsupervised algorithm for tracking whisker movements and quantifying tactile information in freely behaving animals.
  • To analyze whisking kinematics and whisker touch statistics during haptic object exploration.

Main Methods:

  • An unsupervised algorithm was developed to track whisker movements from high-speed video recordings.
  • The algorithm quantifies tactile information statistics without prior knowledge of whisker shape, location, or movement direction.
  • The method demonstrates robustness under challenging conditions like temporary visibility loss and low light/contrast.

Main Results:

  • The algorithm successfully quantified active whisking parameters in mice: speed (protraction: 1,081±322°/s, retraction: 1,564±549°/s), duration (protraction: 34±10 ms, retraction: 24±8 ms), amplitude (40±13°), and frequency (19±7 Hz).
  • Whisker deflection-induced changes in whisking kinematics were quantified.
  • Statistics of whisker touch (speed, amplitude, duration) were calculated.
  • Whisker deprivation was found to not alter whisking kinematics during haptic exploration.

Conclusions:

  • The developed unsupervised algorithm provides a robust method for quantifying whisker-based tactile sensory input in freely behaving animals.
  • This tool enables detailed analysis of whisking behavior and its modulation by tactile feedback.
  • The findings contribute to understanding the neural basis of tactile sensing and sensorimotor control.