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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.

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.

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Related Experiment Video

Updated: Oct 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing.

Youngeun Kim¹, Priyadarshini Panda¹

¹Department of Electrical Engineering, Yale University, New Haven, CT, USA.

Neural Networks : the Official Journal of the International Neural Network Society

|October 18, 2021

Summary

This summary is machine-generated.

This study introduces Spike Activation Lift Training (SALT) and Switched-Batch Normalization (Switched-BN) to enhance deep Spiking Neural Networks (SNNs) for Dynamic Vision Sensor (DVS) data. These methods significantly improve performance and enable training of deeper, more effective SNNs on event-based data.

Keywords:

Dynamic vision sensing Energy-efficient deep learning Event-driven data Neuromorphic computing Spiking neural networks

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Computer Vision
Neuroscience

Background:

Spiking Neural Networks (SNNs) offer low-power computation through event-driven processing.
Existing SNN optimization methods primarily target static datasets, with limited success for Dynamic Vision Sensor (DVS) data.
Shallow SNNs struggle with DVS data, and deeper networks face performance degradation due to diminished spike activity.

Purpose of the Study:

To develop novel algorithmic and architectural solutions for training very deep SNNs on DVS data.
To overcome the limitations of sparse spike activity in deep SNNs, improving information flow and training stability.
To achieve state-of-the-art performance for deep SNNs using event-based vision data.

Main Methods:

Spike Activation Lift Training (SALT): Optimizes weights and thresholds in convolutional layers to increase spike activity across all layers.
Cross-entropy loss is applied after SALT to train network weights effectively.
Switched-Batch Normalization (Switched-BN): A novel architecture applying Batch Normalization to the final layer after accumulating spike information, converting temporal data to a float value.

Main Results:

SALT effectively increases spike activity, enabling deeper information propagation and improved performance in SNNs.
Switched-BN provides significant performance gains with minimal computational overhead by adapting Batch Normalization for SNNs.
Extensive experiments on benchmarks like DVS-Cifar10, N-Caltech, and DHP19 demonstrate the effectiveness of both SALT and Switched-BN.

Conclusions:

The proposed SALT and Switched-BN methods represent a significant advancement in training deep SNNs for DVS data.
These techniques address key challenges in SNN training, particularly the diminishing spike activity in deeper layers.
This work achieves state-of-the-art results for deep SNNs on event-based vision tasks, paving the way for more powerful neuromorphic systems.