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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...

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.

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: Aug 29, 2025

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

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Published on: December 12, 2012

In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual

Doeon Lee¹, Minseong Park¹, Yongmin Baek¹

¹Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.

Nature Communications

|September 6, 2022

Summary

This study introduces a novel in-sensor computing system using a 1-photodiode and 1 memristor (1P-1R) crossbar. This system enables efficient visual cognitive processing directly within sensors, reducing data transfer for machine vision applications.

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Last Updated: Aug 29, 2025

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Published on: December 12, 2012

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Area of Science:

Neuromorphic Engineering
Computer Vision
Materials Science

Background:

Machine vision systems generate vast data, necessitating efficient computational processing.
In-sensor computing offers a solution for reduced data transfer and enhanced energy efficiency in visual processing.
Current in-sensor systems cannot process images stored directly within the sensor.

Purpose of the Study:

To demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing.
To emulate mammalian image encoding for feature extraction directly on the sensor.
To advance the in-sensor computing paradigm by applying trained weights as input voltage.

Main Methods:

Heterogeneous integration of a 1-photodiode and 1 memristor (1P-1R) into a crossbar array.
Emulation of mammalian image encoding processes for feature extraction.
Application of trained weight values as input voltage to the image-saved crossbar array.

Main Results:

Successful demonstration of an in-sensor computing platform capable of processing stored images.
Feature extraction from input images emulating biological visual processing.
Realization of the in-sensor computing paradigm without storing weight values in memristors.

Conclusions:

The developed 1P-1R crossbar enables direct in-sensor visual cognitive processing.
This platform offers an advanced architecture for real-time, data-intensive machine vision.
Bio-stimulus domain reduction enhances efficiency for machine vision applications.