Jove
Visualize
Contact Us
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 Concept Videos

Neural Circuits01:25

Neural Circuits

2.1K
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...
2.1K
Symmetry01:26

Symmetry

61
The equation of an ellipse centered at the origin defines all points whose distances from the center maintain a constant ratio between the horizontal and vertical axes. This equation results in a smooth, closed curve that extends further along the x-axis than the y-axis, giving it a horizontal orientation. Such an ellipse demonstrates three kinds of symmetry: across the x-axis, across the y-axis, and about the origin. These symmetries are essential in understanding the graph's structure and...
61
Perception01:28

Perception

781
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
781

You might also read

Related Articles

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

Sort by
Same author

Sensitivity Evaluation for Global Perturbations in Non-Hermitian Skin-Effect Sensors.

Nanophotonics (Berlin, Germany)·2026
Same author

Amplified spontaneous emission dependence on temperature-induced crystalline phase transition in a solution processed MAPbBr<sub>3</sub> thin film.

Nanoscale·2026
Same author

MoS<sub>2</sub> based 2D material photodetector array with high pixel density.

Nanophotonics (Berlin, Germany)·2025
Same author

Exciton-polariton condensation in MAPbI<sub>3</sub> films from bound states in the continuum metasurfaces.

Nanophotonics (Berlin, Germany)·2025
Same author

Spectral Tuning of Perovskite Laser via Microheaters.

Nano letters·2025
Same author

Enhanced Quantum Magnetometry with a Femtosecond Laser-Written Integrated Photonic Diamond Chip.

Nano letters·2025

Related Experiment Video

Updated: Nov 14, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.1K

Symmetry perception with spiking neural networks.

Jonathan K George1, Cesare Soci2, Mario Miscuglio1

  • 1Department of Electrical and Computer Engineering, George Washington University, Washington, DC, USA.

Scientific Reports
|March 12, 2021
PubMed
Summary

Spiking neural networks can detect mirror symmetry, crucial for visual perception. This method, enhanced by noise, improves recognition of structures in satellite images.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

10.5K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.4K

Related Experiment Videos

Last Updated: Nov 14, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.1K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

10.5K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.4K

Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Mirror symmetry is prevalent in nature and technology, requiring complex organizational processes for detection.
  • Neuromorphic computing, using brain-inspired networks and spiking models, offers efficient solutions for perceptual organization.
  • Current neuromorphic models often neglect time delays, losing critical temporal dynamics of spiking neurons.

Purpose of the Study:

  • To demonstrate how the coincidence detection property in spiking neural networks can enable mirror symmetry detection.
  • To explore the application of this method for automated recognition of man-made structures in geospatial satellite imagery.
  • To investigate the effect of noise on feature detectability and coincidence point generation.

Main Methods:

  • Development of a spiking-based feed-forward neural network algorithm.
  • Testing the algorithm on geospatial satellite image datasets.
  • Analysis of symmetry density and the impact of added noise on image feature detection.

Main Results:

  • The coincidence detection property of the spiking neural network successfully enables mirror symmetry detection.
  • Symmetry density proved effective in distinguishing man-made structures from vegetation in satellite images.
  • The addition of noise enhanced feature detectability by generating more coincidence points.

Conclusions:

  • Spiking neural networks offer a viable pathway for mirror symmetry detection, bridging sensor input and object recognition.
  • This approach has broad applications in areas like computer graphics, robotics, and image analysis.
  • Noise can be beneficial in spiking neural network-based image processing, improving feature recognition.