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Visual System

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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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Related Experiment Video

Updated: Feb 22, 2026

VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

Eye Movement Classification Using Neuromorphic Vision Sensors.

Khadija Iddrisu¹, Waseem Shariff², Maciej Stec²

¹Faculty of Engineering and Computing, Dublin City University, D09DXA0 Dublin, Ireland.

Journal of Eye Movement Research

|February 20, 2026

Summary

This summary is machine-generated.

This study shows Spiking Neural Networks (SNNs) effectively classify eye movements using event cameras. This approach offers a computationally efficient and robust method for neurocognitive diagnostics.

Keywords:

event cameras eye movements spiking neural networks

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Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

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Published on: June 17, 2019

Area of Science:

Neuroscience
Computer Vision
Biologically Inspired Computing

Background:

Eye movement classification (fixations, saccades) is crucial for understanding neurological and cognitive processes.
Conventional RGB cameras struggle with motion blur, latency, and noise, limiting eye-tracking accuracy.
Neuromorphic event cameras (ECs) capture asynchronous, high-temporal-resolution data, ideal for rapid eye movements, but their sparse data challenges traditional algorithms.

Purpose of the Study:

To validate the efficacy of Spiking Neural Networks (SNNs) for eye movement classification using event camera data.
To introduce a novel convolutional SNN architecture designed to process sparse, event-based visual streams.
To establish a benchmark for SNN performance in event-based eye-tracking tasks.

Main Methods:

Manually annotated the EV-Eye dataset, the largest public event-based eye-tracking benchmark, into saccade and fixation sequences.
Developed and implemented a convolutional Spiking Neural Network (SNN) architecture that directly processes spike streams from event cameras.
Benchmarked the proposed SNN model against established spiking networks (SpikingVGG, SpikingDenseNet) and compared computational complexity with Artificial Neural Networks (ANNs).

Main Results:

Achieved 94% accuracy and 0.92 precision in classifying saccades and fixations across data from 10 users.
Demonstrated over a tenfold improvement in computational efficiency compared to Artificial Neural Network (ANN) counterparts.
Highlighted the robustness and efficiency of SNNs for processing sparse, event-based visual data.

Conclusions:

Spiking Neural Networks (SNNs) provide an efficient and robust solution for eye movement classification using event camera data.
This approach holds significant potential for developing fast, low-power neurocognitive diagnostic systems.
The study pioneers the application of SNNs in event-based eye-tracking, setting a new standard for performance and efficiency.