Related Experiment Video
Updated: Feb 2, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
Published on: October 8, 2011
An open-source high-speed infrared videography database to study the principles of active sensing in freely
Alireza Azarfar1, Yiping Zhang1, Artoghrul Alishbayli1
1Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands.
Background:
Active sensing is crucial for navigation. It is characterized by self-generated motor action controlling the accessibility and processing of sensory information. In rodents, active sensing is commonly studied in the whisker system. As rats and mice modulate their whisking contextually, they employ frequency and amplitude modulation. Understanding the development, mechanisms, and plasticity of adaptive motor control will require precise behavioral measurements of whisker position.
Findings:
Advances in high-speed videography and analytical methods now permit collection and systematic analysis of large datasets. Here, we provide 6,642 videos as freely moving juvenile (third to fourth postnatal week) and adult rodents explore a stationary object on the gap-crossing task. The dataset includes sensory exploration with single- or multi-whiskers in wild-type animals, serotonin transporter knockout rats, rats received pharmacological intervention targeting serotonergic signaling. The dataset includes varying background illumination conditions and signal-to-noise ratios (SNRs), ranging from homogenous/high contrast to non-homogenous/low contrast. A subset of videos has been whisker and nose tracked and are provided as reference for image processing algorithms.
Conclusions:
The recorded behavioral data can be directly used to study development of sensorimotor computation, top-down mechanisms that control sensory navigation and whisker position, and cross-species comparison of active sensing. It could also help to address contextual modulation of active sensing during touch-induced whisking in head-fixed vs freely behaving animals. Finally, it provides the necessary data for machine learning approaches for automated analysis of sensory and motion parameters across a wide variety of signal-to-noise ratios with accompanying human observer-determined ground-truth.
Related Concept Videos
The Sense of Self: Reflected Self-Appraisal and Social Comparison
The Uncertainty Principle
Hardy-Weinberg Principle
The Pauli Exclusion Principle
The Aufbau Principle and Hund's Rule
Introduction to Special Senses

