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

Motion contrast classification is a linearly nonseparable problem.

Alireza S Mahani1, Ralf Wessel

  • 1Physics Department, Washington University, St. Louis, MO 63130, USA. amahani@wustl.edu

Neural Computation
|July 27, 2005
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

Towards goal-directed perfusion - Part II: Temperature dependence of Q<sub>10</sub>, oxygen-demand dependence on age and weight, further clinical insights.

Perfusion·2025
Same author

Towards goal-directed perfusion - Part I: Developing physiology-inspired mathematical framework using high-resolution paediatric CPB data.

Perfusion·2025
Same author

Speed modulations in grid cell information geometry.

Nature communications·2025
Same author

Brain-like border ownership signals support prediction of natural videos.

iScience·2025
Same author

Failure in a population: Tauopathy disrupts homeostatic set-points in emergent dynamics despite stability in the constituent neurons.

Neuron·2024
Same author

Brain-like border ownership signals support prediction of natural videos.

bioRxiv : the preprint server for biology·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

Detecting motion contrast, the relative visual field motion, is crucial for vertebrate neurons. This study proves motion contrast detection is linearly nonseparable, requiring nonlinear neural computations.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual System

Background:

  • Many vertebrate neurons in visual pathways are sensitive to image motion contrast.
  • Motion contrast detection is computationally important for visual processing.

Purpose of the Study:

  • To demonstrate that motion contrast detection is a linearly nonseparable classification problem.
  • To establish a theoretical basis for understanding the computational requirements of motion contrast detection.

Main Methods:

  • Proving a theorem that provides a sufficient condition for linear nonseparability.
  • Analyzing the computational properties of motion contrast detection.

Main Results:

  • Motion contrast detection is shown to be linearly nonseparable.

Related Experiment Videos

  • A theorem is presented establishing conditions for linear nonseparability.

    • Conclusions:

    • Linear models are insufficient for explaining motion contrast detection.
    • Nonlinear combinations of local velocity measurements across space and time are necessary for solving the motion contrast problem.