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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.
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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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Updated: Oct 15, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception.

Shuang Wang¹, Chen-Yu Wang¹, Pengfei Wang¹

¹National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

National Science Review

|October 25, 2021

Summary

This study presents a novel neuromorphic vision system that mimics human brain function for faster, more efficient image processing. This brain-inspired system integrates a unique sensor with memristive technology for advanced visual perception.

Keywords:

brain-inspired visual perception memristive crossbar neuromorphic computing retinomorphic sensor van der Waals heterostructure

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Area of Science:

Materials Science
Neuroscience
Computer Engineering

Background:

Conventional machine vision faces limitations in speed and power due to separated sensing and processing.
Neuromorphic vision systems offer a brain-inspired alternative to overcome these challenges.

Purpose of the Study:

To propose and demonstrate a prototype neuromorphic vision system.
To integrate a retinomorphic sensor with a memristive crossbar for enhanced visual perception.

Main Methods:

Fabrication of a retinomorphic sensor using WSe2/h-BN/Al2O3 van der Waals heterostructures.
Networking the sensor with a Pt/Ta/HfO2/Ta one-transistor-one-resistor (1T1R) memristive crossbar.
Demonstration of analog regime image sensing, processing, and recognition.

Main Results:

The system achieved gate-tunable photoresponses mimicking human retinal capabilities.
Fast letter recognition and object tracking were successfully demonstrated.
The integrated system showcases efficient analog-regime visual perception.

Conclusions:

The developed neuromorphic vision system offers a promising solution for high-latency and high-power issues in machine vision.
This brain-inspired approach paves the way for advanced applications in future visual perception.
The successful integration of retinomorphic sensors and memristive crossbars highlights a new direction in AI hardware.