Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Understanding the impact of natural disasters on post-traumatic stress disorder and depression symptoms: An examination of counterfactual displacement scenarios.

PLOS mental health·2026
Same author

Optical linear systems framework for event sensing and computational neuromorphic imaging.

Frontiers in neuroscience·2026
Same author

Event-Based Vision Sensor Lifetime Degradation in Low Earth Orbit.

Sensors (Basel, Switzerland)·2025
Same author

Randomized Controlled Trial of "Bounce Back Now," a Mobile App to Reduce Post-Disaster Symptoms of Posttraumatic Stress, Depressed Mood, and Sleep Disturbance.

The American journal of psychiatry·2025
Same author

The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes.

Neural computation·2025
Same author

Low-latency hierarchical routing of reconfigurable neuromorphic systems.

Frontiers in neuroscience·2025
Same journal

CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain.

Frontiers in neuroscience·2026
Same journal

Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

Frontiers in neuroscience·2026
Same journal

Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair.

Frontiers in neuroscience·2026
Same journal

Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Frontiers in neuroscience·2026
Same journal

Sex-divergent intrinsic brain function in Parkinson's disease: elevated nigral fluctuations and premotor-visuospatial coupling in female patients.

Frontiers in neuroscience·2026
Same journal

Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.

Frontiers in neuroscience·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Oct 24, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.8K

Event Camera Simulator Improvements via Characterized Parameters.

Damien Joubert1, Alexandre Marcireau1, Nic Ralph1

  • 1International Centre for Neuromorphic Systems, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Kingswood, NSW, Australia.

Frontiers in Neuroscience
|August 13, 2021
PubMed
Summary
This summary is machine-generated.

Neuromorphic Dynamic Vision Sensors (DVS) offer faster sensing speeds than conventional sensors. This study introduces an improved DVS pixel simulator to benchmark sensor latency and demonstrate speed advantages.

Keywords:
SNN algorithmSNN benchmarksevent camerasevent-based algorithmssimulator

More Related Videos

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.5K
Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
12:18

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

10.1K

Related Experiment Videos

Last Updated: Oct 24, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.8K
Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.5K
Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
12:18

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

10.1K

Area of Science:

  • Neuromorphic Engineering
  • Computer Vision
  • Sensor Technology

Background:

  • Neuromorphic Dynamic Vision Sensors (DVS) have been developed for over two decades, offering advantages in power efficiency and speed over traditional sensors.
  • While accuracy can be challenging compared to neural networks, DVS sensors excel in data-driven, low-power, and high-speed applications due to sparse, event-based data generation.
  • Modeling real-world pixel behavior, including readout circuitry, is crucial for understanding event timing precision in complex scenarios.

Purpose of the Study:

  • To present an extended Dynamic Vision Sensor (DVS) pixel simulator designed for neuromorphic benchmarking.
  • To simplify latency and noise models within the simulator for more accessible research.
  • To investigate the impact of sensor latency on sensing speed performance using a benchmark task.

Main Methods:

  • Development of an extended DVS pixel simulator incorporating simplified latency and noise models.
  • Inclusion of readout circuitry modeling to enhance the simulation's fidelity to real-world pixel behavior.
  • Benchmarking the simulator's performance using a dynamic variant of the MNIST dataset.

Main Results:

  • The developed simulator allows for a more accurate representation of DVS sensor behavior, including the effects of readout circuitry on event timing.
  • Benchmarking on the dynamic MNIST dataset demonstrated that sensor latency is a key factor enabling DVS sensors to surpass conventional sensors in sensing speed.
  • The simplified models within the simulator facilitate easier use for neuromorphic research and development.

Conclusions:

  • The extended DVS pixel simulator provides a valuable tool for neuromorphic benchmarking and research.
  • Sensor latency is a critical parameter that allows neuromorphic DVS sensors to achieve superior sensing speeds compared to conventional approaches.
  • Further development and application of such simulators can accelerate progress in energy-efficient and high-speed neuromorphic sensing technologies.