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

Visual System01:26

Visual System

1.5K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
1.5K
Signal Flow Graphs01:18

Signal Flow Graphs

550
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
550
Vision01:24

Vision

59.1K
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.
59.1K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

470
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
470
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

390
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
390
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

16.5K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
16.5K

You might also read

Related Articles

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

Sort by
Same author

A Neural Model for V1 That Incorporates Dendritic Nonlinearities and Backpropagating Action Potentials.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same author

Overcoming the limitations of motion sensor models by considering dendritic computations.

Scientific reports·2025
Same author

Plaid masking explained with input-dependent dendritic nonlinearities.

Scientific reports·2024
Same author

State-of-the-art image and video quality assessment with a metric based on an intrinsically non-linear neural summation model.

Frontiers in neuroscience·2023
Same author

Functional Connectome of the Human Brain with Total Correlation.

Entropy (Basel, Switzerland)·2022
Same author

Image Quality Evaluation in Professional HDR/WCG Production Questions the Need for HDR Metrics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022

Related Experiment Video

Updated: Dec 26, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.5K

Visual information flow in Wilson-Cowan networks.

Alexander Gomez-Villa1, Marcelo Bertalmío1, Jesus Malo2

  • 1Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

Journal of Neurophysiology
|March 12, 2020
PubMed
Summary

This study analyzes the communication efficiency of neural networks simulating the retina-V1 pathway. Results show Wilson-Cowan networks substantially reduce redundancy, confirming efficient coding and suggesting applications in image compression.

Keywords:
Wilson–Cowan equationsdivisive normalizationefficient representation principlemulti-informationtotal correlation

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.6K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.1K

Related Experiment Videos

Last Updated: Dec 26, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.5K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.6K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.1K

Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Information Theory

Background:

  • The retina-V1 pathway's communication efficiency is crucial for visual processing.
  • Existing models often focus on statistical independence, but biological systems may use different principles.
  • Wilson-Cowan networks offer a biologically plausible model for neural interactions.

Purpose of the Study:

  • To analyze the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers simulating the retina-V1 pathway.
  • To investigate the reduction of total correlation in neural responses along this simulated pathway.
  • To explore the potential of neural field models for image compression.

Main Methods:

  • First-time analysis of Wilson-Cowan networks using multivariate total correlation.
  • Derivation of cortical model parameters from the relationship between Wilson-Cowan and divisive normalization models.
  • Theoretical expression for total correlation reduction and empirical study using natural scenes and advanced statistical tools for estimating multivariate total correlation.

Main Results:

  • The cascade of layers substantially reduces redundancy between neural responses, despite not being optimized for statistical independence.
  • Wilson-Cowan networks exhibit similar efficiency to equivalent divisive normalization models.
  • Nonlinear local contrast computation and oriented filters contribute most significantly to total correlation reduction.

Conclusions:

  • Psychophysically tuned models are more efficient in regions with higher luminance-contrast.
  • The findings provide an alternative confirmation of the efficient coding hypothesis for Wilson-Cowan systems.
  • Neural field models are suggested as a viable alternative to divisive normalization for image compression.