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 Experiment Videos

Edge detection based on Hodgkin-Huxley neuron model simulation.

Hayat Yedjour1, Boudjelal Meftah2, Olivier Lézoray3

  • 1Laboratoire Signal Image et Parole (SIMPA), Université Mohamed Boudiaf (USTO), Oran, Algeria. hayat.yedjour@univ-usto.dz.

Cognitive Processing
|April 5, 2017
PubMed
Summary
This summary is machine-generated.

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

Assessing the quality of whole slide images in cytology from nuclei features.

Journal of pathology informatics·2025
Same author

A spatiotemporal energy model based on spiking neurons for human motion perception.

Cognitive neurodynamics·2024
Same author

Extraction of the association rules from artificial neural networks based on the multiobjective optimization.

Network (Bristol, England)·2022
Same author

Multi-features extraction based on deep learning for skin lesion classification.

Tissue & cell·2021
Same author

Depth-Guided Disocclusion Inpainting of Synthesized RGB-D Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2016
Same author

Exemplar-based inpainting: technical review and new heuristics for better geometric reconstructions.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2015
Same journal

The STEARC effect revisited: does time shape spatial attention and memory?

Cognitive processing·2026
Same journal

Higher- and lower-level processing in strategic reading: Reconceptualising the Survey of Reading Strategies (SORS).

Cognitive processing·2026
Same journal

More caution or more lenient: deciphering the role of negative affect in recognition and inference.

Cognitive processing·2026
Same journal

Cognitive offloading, critical thinking and attitudes towards artificial intelligence in the era of ChatGPT: a comparative study of artificial intelligence-assisted and manual task performance in young adults.

Cognitive processing·2026
Same journal

Emojis vs. black-and-white and colored drawings: comparing living and non-living things in oral naming.

Cognitive processing·2026
Same journal

The impact of facial expressions on space- and object-based attention by gaze cues.

Cognitive processing·2026
See all related articles

This study introduces a biologically inspired spiking neural network for image edge detection. The model mimics the human visual cortex, achieving high performance in contour detection tasks.

Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Artificial Intelligence

Background:

  • Natural vision systems utilize specialized neurons, like simple cells in the human primary visual cortex, for orientation selectivity.
  • Existing edge detection methods may not fully capture the complex, biologically plausible mechanisms of visual processing.

Purpose of the Study:

  • To propose a novel spiking neural network (SNN) model for image edge detection.
  • To emulate the orientation-selective properties of simple cells in the human visual cortex.
  • To evaluate the model's performance against established edge detection techniques.

Main Methods:

  • Developed a feedforward SNN incorporating conductance-based Hodgkin-Huxley neuron models.
  • Utilized Gabor receptive fields to simulate orientation selectivity.
Keywords:
Computational neuroscienceEdge detectionGabor functionHodgkin–Huxley modelSpiking neural networksVisual cortex

Related Experiment Videos

  • Generated orientation maps based on neuronal firing rates.
  • Simulated and compared the model's performance on natural image datasets.

    • Main Results:

    • The SNN model successfully generated orientation maps.
    • Computer simulations demonstrated successful edge and contour detection.
    • Comparative analysis showed competitive or superior performance against five other edge detection methods.
    • Evaluation on a public dataset with ground truths confirmed the model's efficacy.

    Conclusions:

    • The proposed biologically inspired SNN model is effective for image edge and contour detection.
    • The model's architecture, inspired by the visual cortex, offers a promising approach for computer vision tasks.
    • Further research can explore more complex visual processing mechanisms within SNN frameworks.