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Related Experiment Video

Updated: Oct 22, 2025

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

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Published on: April 16, 2014

A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions.

Emre Baspinar¹, Luca Calatroni², Valentina Franceschi³

¹INRIA Sophia Antipolis Méditerranée, MathNeuro, 06902 Sophia Antipolis, France.

Journal of Imaging

|August 30, 2021

Summary

This study enhances computational models of visual perception, using a novel sub-Riemannian kernel to better explain orientation-dependent illusions like the Poggendorff effect in the brain

Keywords:

Wilson-Cowan modelling cortical-inspired imaging local histogram equalisation sub-Riemannian heat kernel visual illusions

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Published on: July 21, 2020

Area of Science:

Computational neuroscience
Mathematical modeling of visual illusions

Background:

The Poggendorff illusion demonstrates orientation-dependent visual misperceptions.
Existing models struggle to fully capture the complex neural processing in the visual cortex (V1).

Purpose of the Study:

To develop an improved Wilson-Cowan model for describing orientation-dependent Poggendorff-like illusions.
To incorporate the anisotropic functional architecture of V1 into a computational model.

Main Methods:

Embedding a sub-Riemannian heat kernel into the neuronal interaction term of Wilson-Cowan models.
Utilizing gradient descent and Fourier-based methods for numerical computation of sub-Laplacian evolution.
Comparing the new model with previously proposed cortical-inspired approaches.

Main Results:

The sub-Riemannian kernel approach provides a stronger numerical reproduction of visual misperceptions.
The model successfully captures inpainting-type biases observed in visual processing.
The enhanced model aligns better with the anisotropic nature of V1 connections.

Conclusions:

The sub-Riemannian heat kernel offers a more effective mathematical tool for modeling visual illusions.
This approach improves our understanding of neural mechanisms underlying visual perception and V1 function.