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

Spatial decorrelation in orientation-selective cortical cells

A Dimitrov1, J D Cowan

  • 1Department of Mathematics, University of Chicago, IL 60637, USA.

Neural Computation
|September 23, 1998
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

Observation of WWγ Production and Search for Hγ Production in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2024
Same author

New Structures in the J/ψJ/ψ Mass Spectrum in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2024
Same author

Search for Scalar Leptoquarks Produced via τ-Lepton-Quark Scattering in pp Collisions at sqrt[s]=13  TeV.

Physical review letters·2024
Same author

Search for Inelastic Dark Matter in Events with Two Displaced Muons and Missing Transverse Momentum in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2024
Same author

Evidence for the Higgs Boson Decay to a Z Boson and a Photon at the LHC.

Physical review letters·2024
Same author

Probing Small Bjorken-x Nuclear Gluonic Structure via Coherent J/ψ Photoproduction in Ultraperipheral Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

Physical review letters·2024
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

We developed a model for visual cortex lateral connectivity, uncovering new statistical structures in visual input. This redundancy reduction model explains how orientation-selective circuits form networks, aligning with physiological data.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual System Modeling

Background:

  • The visual cortex processes complex visual information.
  • Understanding the lateral connectivity of orientation-selective cells is crucial for visual processing.
  • Existing models may not fully capture the statistical properties of visual input signals.

Purpose of the Study:

  • To propose a novel model for the lateral connectivity of orientation-selective cells in the visual cortex.
  • To identify and analyze new statistical structures within the visual cortex input signal.
  • To derive the network structure based on the principle of redundancy reduction.

Main Methods:

  • Analysis of input signal properties to the visual cortex.
  • Derivation of lateral connectivity based on redundancy reduction principles.

Related Experiment Videos

  • Modeling of orientation-selective local circuits and their spatial network structure.
  • Comparison of model predictions with physiological measurements.

    • Main Results:

    • Identification of novel statistical structures in the visual input signal.
    • Derivation of a specific lateral connectivity pattern for orientation-selective circuits.
    • Construction of a complete spatial network structure from these local circuits.
    • Model results show agreement with existing physiological data.

    Conclusions:

    • The proposed model provides a framework for understanding visual cortex lateral connectivity.
    • Redundancy reduction is a key principle shaping the structure of visual networks.
    • The model successfully predicts network organization based on input signal statistics.