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

Color Vision01:24

Color Vision

2.0K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
2.0K
Vision01:24

Vision

61.7K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
61.7K
Perceptual Constancy01:12

Perceptual Constancy

1.8K
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
1.8K
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

11.2K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
11.2K
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

12.0K
The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...
12.0K
Visual System01:26

Visual System

2.3K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
2.3K

You might also read

Related Articles

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

Sort by
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026

Related Experiment Video

Updated: Apr 14, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.8K

Primary Visual Cortex Contributes to Color Constancy by Predicting Rather than Discounting the Illuminant.

Shaobing Gao1, Yongjie Li2

  • 1College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.

International Journal of Neural Systems
|April 12, 2026
PubMed
Summary
This summary is machine-generated.

This study models the primary visual cortex (V1) to understand color constancy (CC). Findings show V1 double-opponent (DO) neurons encode illuminant color, supporting CC by encoding rather than discounting light.

Keywords:
V1color constancycomputational modelingreceptive field

More Related Videos

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.6K
Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex
08:42

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Published on: February 8, 2020

11.6K

Related Experiment Videos

Last Updated: Apr 14, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.8K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.6K
Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex
08:42

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Published on: February 8, 2020

11.6K

Area of Science:

  • Neuroscience
  • Computational Vision
  • Visual Perception

Background:

  • Color constancy (CC) enables stable color perception under varying illumination.
  • The role of the primary visual cortex (V1) in CC is not fully understood.
  • Double-opponent (DO) neurons in V1 are hypothesized to contribute to CC, but their mechanism remains unclear.

Purpose of the Study:

  • To computationally investigate the role of V1 neurons in color constancy.
  • To model the function of V1 DO neurons in processing illuminant information.
  • To provide evidence for V1's contribution to CC through illuminant encoding.

Main Methods:

  • Developed an electrophysiologically-based V1 neural model.
  • Trained the model on natural images with ground truth illuminants.
  • Analyzed the properties of model neurons, comparing them to biological V1 neurons.

Main Results:

  • Learned model neurons exhibit receptive field properties similar to V1 simple and DO cells.
  • V1 DO neurons demonstrated more robust performance in illuminant prediction than simple cells.
  • Computational evidence suggests V1 DO neurons encode illuminant color.

Conclusions:

  • V1 DO neurons contribute to color constancy by encoding illuminant information.
  • This encoding mechanism offers an alternative to the illuminant discounting hypothesis.
  • Findings may advance understanding of visual mechanisms and computer vision algorithms.