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Related Concept Videos

Visual System01:26

Visual System

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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.
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Vision01:24

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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.
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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Parallel Processing01:20

Parallel Processing

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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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Anatomy of the Eyeball01:20

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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...
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Color Vision01:24

Color Vision

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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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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
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Visual Perception: Neural Networks for Stereopsis.

Jenny C A Read1, Bruce G Cumming2

  • 1Newcastle University, Institute of Neuroscience, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK.

Current Biology : CB
|June 21, 2017
PubMed
Summary
This summary is machine-generated.

Our brains perceive depth using binocular disparities, the differences between images seen by each eye. A neural network trained on natural images learned properties matching the visual cortex, highlighting cells sensing these binocular differences.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Computer Vision

Background:

  • Depth perception is crucial for navigating the environment.
  • The brain processes binocular disparities to construct a 3D visual world.
  • Understanding the neural mechanisms of depth perception remains a key challenge.

Purpose of the Study:

  • To investigate how neural networks can model the brain's depth perception mechanisms.
  • To determine if artificial neural networks can learn key properties of the human visual cortex related to binocular vision.
  • To explore the role of binocular disparity processing in artificial and biological systems.

Main Methods:

  • Training a convolutional neural network on a large dataset of natural binocular images.
  • Analyzing the learned parameters and receptive field properties of the network.
  • Comparing network properties to known characteristics of neurons in the primate visual cortex.

Main Results:

  • The trained neural network learned parameters that closely match key properties observed in the visual cortex.
  • The network effectively utilized binocular disparity information for depth estimation.
  • Cells within the network, analogous to visual cortex neurons, predominantly responded to binocular differences.

Conclusions:

  • Artificial neural networks can serve as valuable models for understanding biological vision.
  • Binocular disparity is a fundamental cue for depth perception, processed effectively by learned network architectures.
  • This work provides insights into the neural computations underlying 3D visual perception.