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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.

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...

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.

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Video

Updated: Sep 3, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions.

Byron H Price¹, Jeffrey P Gavornik¹

¹Center for Systems Neuroscience, Graduate Program in Neuroscience, Department of Biology, Boston University, Boston, MA, United States.

Frontiers in Computational Neuroscience

|July 25, 2022

Summary

This summary is machine-generated.

The brain uses efficient coding principles to make predictions by removing redundant information. This theory explains neural computations in the retina and early visual cortex, particularly in rodents.

Keywords:

efficient coding predictive coding temporal representations time visual cortex

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Area of Science:

Neuroscience
Computational Neuroscience
Information Theory

Background:

The brain's predictive capabilities are widely accepted but poorly understood.
Accurate prediction necessitates neural circuits learning and storing spatiotemporal patterns.
Information theory offers a framework for analyzing efficient neural coding schemes.

Purpose of the Study:

To clarify the relationship between efficient coding and temporal prediction in the brain.
To review evidence supporting efficient coding in retinal computations.
To apply efficient coding principles to early visuocortical processing.

Main Methods:

Theoretical analysis linking information theory to neural prediction.
Review of existing experimental data on retinal and visual cortex computations.
Application of efficient coding models to rodent visual processing data.

Main Results:

Efficient coding, by removing redundancy, is a viable model for neural data storage and transfer.
Evidence suggests efficient coding principles explain computations in the retina.
Rodent data aligns with efficient coding predictions for early visual cortex functions.

Conclusions:

Efficient coding provides a unifying framework for understanding neural prediction across different brain areas.
The theory can guide future experiments investigating how neural circuits predict environmental statistics.
Further research is needed to explore the extent of efficient coding in neural prediction.