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Vision

Vision

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.

Anatomy of the Eyeball

Anatomy of the Eyeball

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...

The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

Visual System

Visual System

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...

Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

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Color Vision

Color Vision

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.

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Related Experiment Video

Updated: Feb 24, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Prewired static visual receptive fields for environment-agnostic perception.

Minjun Kang¹, Seungdae Baek², Se-Bum Paik^1,2

¹Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.

Patterns (New York, N.Y.)

|February 23, 2026

Summary

This summary is machine-generated.

Biological brains adapt to changing environments, unlike conventional deep neural networks (DNNs). This study shows static visual filters in DNNs enable robust continual learning and environment-agnostic object recognition.

Keywords:

visual system, continual learning, domain shift, deep neural network, receptive field, hard-wired circuit, latent space clustering, object recognition, overfitting, general representation

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Related Experiment Videos

Last Updated: Feb 24, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Area of Science:

Computational neuroscience
Artificial intelligence
Computer vision

Background:

Biological brains exhibit remarkable adaptability to dynamic environments.
Conventional deep neural networks (DNNs) struggle with domain shifts, limiting their real-world applicability.
Early visual pathways in biological brains develop static receptive fields that aid in environment-agnostic recognition.

Purpose of the Study:

To investigate if static, hard-wired receptive fields can improve the domain generalization of deep neural networks.
To determine if mimicking biological visual processing enhances continual learning in artificial systems.
To explore the impact of fixed early-layer filters on object recognition robustness.

Main Methods:

Implemented pre-developed Gabor filters in the early layers of DNNs, keeping them static during training.
Trained and tested networks under significant domain shifts.
Compared the generalization capabilities and representational properties of the modified DNNs against conventional DNNs.

Main Results:

Networks with static Gabor filters demonstrated robust continual learning and generalization across domains.
Static filters prevented local texture biases, promoting shape-based perception akin to primates.
Modified DNNs achieved generalized latent space representations, unlike conventional DNNs capturing domain-specific variance.

Conclusions:

Static, hard-wired receptive fields are a key biological strategy for reliable continual learning in dynamic environments.
Incorporating biologically inspired static filters into DNNs enhances their robustness to domain shifts.
This approach offers a pathway towards more adaptable and generalizable artificial intelligence systems.