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

Extending the Stabilized Supralinear Network model for binocular image processing.

Ben Selby1, Bryan Tripp1

  • 1Department of Systems Design Engineering, University of Waterloo, 200 University Ave W., Waterloo, Ontario, Canada N2L 3G1; Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave W., Waterloo, Ontario, Canada N2L 3G1.

Neural Networks : the Official Journal of the International Neural Network Society
|April 8, 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

A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study.

JMIR AI·2026
Same author

Multimodal Artificial Intelligence in Medicine.

Kidney360·2024
Same author

Modeling the Role of Contour Integration in Visual Inference.

Neural computation·2023
Same author

Accelerated sequential bilateral theta-burst stimulation in major depression: an open trial.

European archives of psychiatry and clinical neuroscience·2023
Same author

Consistency of frontal cortex metabolites quantified by magnetic resonance spectroscopy within overlapping small and large voxels.

Scientific reports·2023
Same author

Associations between locus coeruleus integrity and diagnosis, age, and cognitive performance in older adults with and without late-life depression: An exploratory study.

NeuroImage. Clinical·2022
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

The Stabilized Supralinear Network (SSN) model can be extended for image input and tested in binocular vision, showing promise for complex visual cortex models. This computational neuroscience approach enhances understanding of visual processing.

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Computer Vision

Background:

  • The visual cortex is complex, requiring computational models to link local mechanisms with high-level functions.
  • The Stabilized Supralinear Network (SSN) model successfully explains V1 receptive field phenomena and predicts new properties.
  • Assessing the SSN's suitability for large-scale visual cortex models is crucial for advancing computational vision.

Purpose of the Study:

  • To evaluate the extensibility and computational tractability of the SSN model for large-scale visual cortex simulations.
  • To determine if the SSN can be adapted to process image inputs and function in a binocular context.
  • To assess the scalability of the SSN when reformulated as a convolutional neural network.

Main Methods:

Keywords:
Balanced excitation/inhibitionConvolutional neural networksInterocular transfer of suppressionPrimary visual cortexStabilized supralinear network

Related Experiment Videos

  • Extended the SSN to accept image inputs via a linear-nonlinear stage.
  • Investigated the SSN's performance in a binocular context, specifically interocular transfer of surround suppression.
  • Reformulated the SSN as a convolutional neural network to evaluate its performance on parallel hardware.

    • Main Results:

    • The extended SSN model demonstrated similar behavior when processing image inputs.
    • The SSN successfully reproduced data on interocular transfer of surround suppression, indicating binocular function.
    • The convolutional neural network reformulation of the SSN showed good scalability on parallel hardware.

    Conclusions:

    • The SSN is a plausible model for lateral interactions in V1.
    • The SSN is well-suited as a component for building complex computational models of the visual cortex.
    • Future research will leverage the SSN in large networks to explore vision processes.