Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Analogue speech recognition based on physical computing.

Nature·2025

Same author

Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity.

Nature communications·2025

Same author

The road to commercial success for neuromorphic technologies.

Nature communications·2025

Same author

The inherent adversarial robustness of analog in-memory computing.

Nature communications·2025

Same author

The neurobench framework for benchmarking neuromorphic computing algorithms and systems.

Nature communications·2025

Same author

Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing.

Nature computational science·2025

Search research articles

Related Experiment Video

Updated: Aug 14, 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

Adversarial attacks on spiking convolutional neural networks for event-based vision.

Julian Büchel¹, Gregor Lenz², Yalun Hu³

¹IBM Research, Zurich, Switzerland.

Frontiers in Neuroscience

|January 13, 2023

Summary

This study adapts adversarial attacks for event-based vision systems, demonstrating higher success rates with smaller perturbations on spiking neural networks. These findings are verified on neuromorphic hardware, highlighting security vulnerabilities in this emerging field.

Keywords:

adversarial examples dynamic vision sensors neuromorphic engineering robust AI spiking convolutional neural networks

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Related Experiment Videos

Last Updated: Aug 14, 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

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

Computer Vision
Neuromorphic Engineering
Artificial Intelligence Security

Background:

Event-based dynamic vision sensors offer sparse, low-power data suitable for specialized applications.
Convolutional spiking neural networks (SNNs) leverage this event-based data for energy efficiency on neuromorphic hardware.
The security of SNNs against adversarial attacks in event-based vision remains largely unexplored.

Purpose of the Study:

To adapt white-box adversarial attack algorithms for discrete, sparse event-based visual data.
To evaluate the effectiveness of these adapted attacks against SNNs.
To verify attack efficacy directly on neuromorphic hardware.

Main Methods:

Adapted existing white-box adversarial attack algorithms for the unique characteristics of event-based data.
Quantified perturbation magnitudes and success rates compared to state-of-the-art methods.
Performed direct hardware verification of adversarial perturbations on neuromorphic systems.

Main Results:

Achieved higher success rates with significantly smaller perturbation magnitudes than current algorithms.
Demonstrated the practical effectiveness of adversarial attacks on event-based SNNs.
Verified attack efficacy through direct testing on neuromorphic hardware.

Conclusions:

Event-based spiking neural networks are vulnerable to adapted adversarial attacks.
Perturbations can be smaller and more successful than previously shown.
Further research is needed on defense strategies like adversarial training for neuromorphic systems.