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Related Experiment Video

Updated: Oct 19, 2025

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

Active inference through whiskers.

Francesco Mannella¹, Federico Maggiore¹, Manuel Baltieri²

¹Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.

Neural Networks : the Official Journal of the International Neural Network Society

|September 26, 2021

Summary

This summary is machine-generated.

Rodents anticipate object contact using active inference, modulating whisker movements to minimize prediction errors during environmental exploration. This active sensing strategy optimizes whisking for expected and unexpected encounters.

Keywords:

Active inference Active sensing Whiskers

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Related Experiment Videos

Last Updated: Oct 19, 2025

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

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

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Published on: November 21, 2019

Flat-floored Air-lifted Platform: A New Method for Combining Behavior with Microscopy or Electrophysiology on Awake Freely Moving Rodents

Flat-floored Air-lifted Platform: A New Method for Combining Behavior with Microscopy or Electrophysiology on Awake Freely Moving Rodents

Published on: June 29, 2014

Area of Science:

Neuroscience
Robotics
Sensory Systems

Background:

Rodents actively use whisking for environmental exploration and object localization.
Whisking control exhibits anticipatory behaviors, targeting protraction to expected object distances.

Purpose of the Study:

Characterize rodent whisking control as an active inference process.
Model how rodents minimize prediction errors during whisker-object interactions.

Main Methods:

Formulate an active inference model for whisking control.
Qualitatively reproduce empirical findings on whisker amplitude modulation.

Main Results:

The active inference model explains anticipatory whisking adjustments.
The model accounts for minimizing sensory prediction errors from expected/unexpected contacts.

Conclusions:

Rodent whisking exemplifies active inference in biological active sensing.
Active inference provides a framework for understanding whisking neurobiology.