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

Internal models for visual perception.

Wolfram Erlhagen1

  • 1Departamento de Matemática, Universidade do Minho-Campus de Azurém, 4800-058 Guimarães, Portugal. wolfram.erlhagen@mct.uminho.pt

Biological Cybernetics
|May 17, 2003
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

Robust working memory in a two-dimensional continuous attractor network.

Cognitive neurodynamics·2024
Same author

Brain-inspired multiple-target tracking using Dynamic Neural Fields.

Neural networks : the official journal of the International Neural Network Society·2022
Same author

A dynamic neural field model of continuous input integration.

Biological cybernetics·2021
Same author

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect.

Journal of biomechanics·2021
Same author

Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning.

Neural networks : the official journal of the International Neural Network Society·2015
Same author

Review of Robotic Technology for Stereotactic Neurosurgery.

IEEE reviews in biomedical engineering·2015
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles

This study models how the brain predicts future motion, revealing an adaptation mechanism that improves motion extrapolation and object permanence during occlusion.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Perception

Background:

  • Perceptual extrapolation of past experiences into the future is crucial for daily life.
  • The neural mechanisms underlying motion-induced extrapolation are not fully understood.

Purpose of the Study:

  • To investigate the neuronal population response to moving stimuli using a network model.
  • To propose and analyze an adaptation mechanism for modulating motion extrapolation.
  • To explore the functional advantages of an internally generated model for stimulus processing.

Main Methods:

  • A network model with interacting excitatory and inhibitory cell populations was developed.
  • The model simulates neuronal responses to continuously moving stimuli.
  • An occluder paradigm was used to test the model's predictions.

Related Experiment Videos

Main Results:

  • An adaptation mechanism was proposed that controls motion extrapolation without altering network structure.
  • Integration of an internally generated model enhances input stream recognition speed and reliability.
  • The model demonstrates object permanence following stimulus occlusion.

Conclusions:

  • The proposed adaptation mechanism offers a way to modulate motion extrapolation.
  • Internally generated models of moving stimuli improve perceptual processing and object tracking.
  • Modeling results align with experimental findings on motion-induced extrapolation.