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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.

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Related Experiment Video

Updated: May 11, 2026

How to Create and Use Binocular Rivalry

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

Derived patterns in binocular rivalry networks.

Casey O Diekman¹, Martin Golubitsky², Yunjiao Wang³

¹Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, 43210, USA.

Journal of Mathematical Neuroscience

|May 10, 2013

Summary

This summary is machine-generated.

Wilson

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Last Updated: May 11, 2026

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Area of Science:

Neuroscience
Computational Neuroscience
Visual Perception

Background:

Binocular rivalry involves alternating visual perception when different images are presented to each eye.
Wilson's neuronal network models generalize rivalry to multiple competing patterns.
These models identify rivalry with time-periodic states of pattern dominance.

Purpose of the Study:

To investigate if Wilson's neuronal networks can support unlearned, or 'derived,' patterns.
To explain the perception of derived patterns observed in binocular rivalry experiments.
To modify Wilson networks to incorporate lateral coupling for modeling primary visual cortex structure.

Main Methods:

Constructing modified Wilson networks tailored to specific binocular rivalry experiments.
Implementing symmetry breaking within the modified networks.
Introducing lateral coupling inspired by the primary visual cortex structure.

Main Results:

Demonstrated that Wilson networks can support derived patterns not explicitly learned by the network.
Modified networks successfully predicted surprising outcomes in binocular rivalry experiments.
Lateral coupling in modified networks explains the perception of derived patterns.

Conclusions:

Neuronal network models can account for the perception of derived patterns in binocular rivalry.
The inclusion of lateral coupling is crucial for accurately modeling visual cortex function in rivalry.
Modified Wilson networks provide a framework for understanding complex perceptual phenomena in binocular rivalry.