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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.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

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...

Color Vision

Color Vision

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.

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...

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Related Experiment Video

Updated: Apr 28, 2026

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Event-Based Vision at the Edge: A Review.

Michael Middleton¹, Teymoor Ali², Epifanios Baikas³

¹Department of Electronic Engineering, University of York, York YO10 5DD, UK.

|April 27, 2026

Summary

This summary is machine-generated.

Spiking Neural Networks (SNNs) offer energy-efficient edge AI but face deployment hurdles. Bridging gaps in datasets, training, and hardware integration is crucial for realizing SNN-based vision potential.

Keywords:

neuromorphic computing neuromorphic hardware spiking neural networks

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Published on: March 25, 2011

Area of Science:

Neuromorphic Engineering
Artificial Intelligence
Computer Vision

Background:

Spiking Neural Networks (SNNs) on neuromorphic hardware promise energy-efficient, low-latency inference for edge devices.
Challenges exist in creating a clear research pathway for deploying neuromorphic devices.

Purpose of the Study:

To provide a structured review and position on the state of SNN-based vision.
To identify critical integration challenges hindering edge deployment.

Main Methods:

Surveyed SNN network architectures (convolutional, Transformers, hybrid).
Examined training methodologies (surrogate gradient, ANN-to-SNN conversion).
Catalogued event-based datasets and simulation techniques.
Assessed neuromorphic computing hardware platforms.

Main Results:

Individual areas like network design and training have matured, but integration remains a challenge.
Event-based datasets are scarce and lack standardization.
Training methods create gaps between simulation and deployment hardware.
Neuromorphic platform access is limited by proprietary toolchains.

Conclusions:

Advancing individual SNN components is less critical than addressing integration challenges.
Bridging gaps in datasets, training, and hardware access is key to unlocking SNN-based vision at the edge.