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

Computational models of cortical visual processing

D J Heeger1, E P Simoncelli, J A Movshon

  • 1Department of Psychology, Stanford University, CA 94305, USA.

Proceedings of the National Academy of Sciences of the United States of America
|January 23, 1996
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

Image compression via joint statistical characterization in the wavelet domain.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Neuronal basis of the motion aftereffect reconsidered.

Neuron·2001
Same author

Human cortical activity correlates with stereoscopic depth perception.

Journal of neurophysiology·2001
Same author

Effects of experimental strabismus on the architecture of macaque monkey striate cortex.

The Journal of comparative neurology·2001
Same author

Natural image statistics and neural representation.

Annual review of neuroscience·2001
Same author

Natural signal statistics and sensory gain control.

Nature neuroscience·2001
Same journal

Chemotactic self-organization captures the dynamics of mammalian hair follicle patterning.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Tomographic imaging of superconducting order using particle-hole interference.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Analytical modeling for suction cup designs for skin-interfaced wearable devices.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Improving cell-free metabolism through direct integration of artificial respiratory chains.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

This study introduces two computational models for visual cortex neurons, moving beyond linear systems to better explain simple cells in V1 and motion-selective cells in MT. These models suggest a unified computational strategy across the cerebral cortex.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual System Research

Background:

  • Early work by Hubel and Wiesel characterized visual cortex neuron responses using qualitative methods.
  • Formal descriptions of neural transformations of visual input have evolved, often using linear-systems approaches.
  • Purely linear models are insufficient for accurately describing cortical neuron responses.

Purpose of the Study:

  • To present two related computational models for visual cortex neurons.
  • To model responses of simple cells in primary visual cortex (V1).
  • To model pattern direction selective cells in the MT (V5) area involved in motion analysis.

Main Methods:

  • Development of two related computational models with a shared structure.

Related Experiment Videos

  • Application of models to understand V1 simple cell and MT pattern direction selective cell responses.
  • Utilizing Macintosh microcomputer implementations for model exploration.

    • Main Results:

    • The models provide a framework for understanding visual cortex neuron computations.
    • Demonstrated a common structural approach for different visual processing tasks.
    • Highlighted the inadequacy of purely linear models for cortical processing.

    Conclusions:

    • The proposed models offer a more accurate account of visual cortex neuron function.
    • Suggests a consistent computational strategy across different areas of the cerebral cortex.
    • Model implementations are available for further research and exploration.