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 Concept Videos

Neural Circuits01:25

Neural Circuits

1.9K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.9K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

320
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
320
Perceptual Constancy01:12

Perceptual Constancy

705
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
705
Vision01:24

Vision

56.7K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
56.7K
Color Vision01:24

Color Vision

942
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
942
Effects of feedback01:24

Effects of feedback

763
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
763

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Consciousness indicators, mimicry, and internal variants.

Trends in cognitive sciences·2026
Same author

Modality-agnostic decoding of vision and language from fMRI.

eLife·2026
Same author

Identifying indicators of consciousness in AI systems.

Trends in cognitive sciences·2025
Same author

Evidence for compositionality in fMRI visual representations via Brain Algebra.

Communications biology·2025
Same author

Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics.

ArXiv·2025
Same author

Through their eyes: Multi-subject brain decoding with simple alignment techniques.

Imaging neuroscience (Cambridge, Mass.)·2025

Related Experiment Video

Updated: Oct 21, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

699

Predictive coding feedback results in perceived illusory contours in a recurrent neural network.

Zhaoyang Pang1, Callum Biggs O'May1, Bhavin Choksi1

  • 1CerCO, CNRS UMR5549, Toulouse, France.

Neural Networks : the Official Journal of the International Neural Network Society
|September 9, 2021
PubMed
Summary

Recurrent neural networks with feedback connections can perceive illusory contours, similar to human vision. This brain-inspired approach enhances computer vision by incorporating feedback error correction for better contour detection.

Keywords:
Deep learningFeedbackGenerative modelsIllusory contoursKanizsa squaresPredictive coding

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.1K

Related Experiment Videos

Last Updated: Oct 21, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

699
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.1K

Area of Science:

  • Computer Vision
  • Computational Neuroscience

Background:

  • Modern convolutional neural networks (CNNs) excel at computer vision but do not fully replicate human visual perception.
  • Humans perceive illusory contours, a phenomenon not well-understood by current feedforward CNNs, potentially involving feedback connections in the visual cortex.

Purpose of the Study:

  • To investigate whether recurrent feedback neural networks can perceive illusory contours akin to human vision.
  • To explore the role of brain-inspired recurrent dynamics in enhancing CNNs' visual perception capabilities.

Main Methods:

  • Equipped a deep feedforward CNN with brain-inspired recurrent dynamics, implementing an iterative "predictive coding" feedback mechanism.
  • Pretrained the network with unsupervised reconstruction on natural images, followed by finetuning on a form discrimination task.
  • Tested the model's perception of illusory contours (Kanizsa squares) and analyzed image reconstructions.

Main Results:

  • The recurrent network classified illusory contours as physical squares more frequently than feedforward baselines.
  • The model demonstrated genuine perception of illusory contours, evidenced by measurable changes in image luminance profiles.
  • Ablation studies confirmed the critical roles of natural image pretraining and feedback error correction in illusion perception.

Conclusions:

  • Recurrent feedback dynamics enable CNNs to perceive illusory contours, bridging a gap between artificial and human visual perception.
  • The findings suggest that incorporating feedback mechanisms is crucial for developing more human-like computer vision systems.
  • The study validates the effectiveness of predictive coding feedback in deeper networks like VGG for illusory contour perception.