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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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Color Vision01:24

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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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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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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Vision: Dialogues between Deep Networks and the Brain.

Charles E Connor1

  • 1Department of Neuroscience, Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA.

Current Biology : CB
|July 10, 2019
PubMed
Summary

Deep neural networks interacting with the brain can generate visual images. These generated images elicit strong neural responses, reflecting visual information within the brain.

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Deep neural networks (DNNs) are increasingly used to model brain function.
  • Understanding how artificial systems interact with biological neural networks is crucial.

Purpose of the Study:

  • To investigate the capacity of DNNs to generate visual stimuli.
  • To assess the neural responses elicited by DNN-generated images in the brain.

Main Methods:

  • Utilizing deep neural networks to synthesize visual images.
  • Presenting these generated images to subjects and recording neural activity.

Main Results:

  • DNN-generated images produced significant neural responses in the brain.

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  • The neural responses suggest that the generated images capture aspects of visual information processing.

    • Conclusions:

    • Deep neural networks can create visual stimuli that strongly engage the brain.
    • This research highlights the potential of AI-brain interactions for understanding visual perception.