Visual Information Flow in Wilson Cowan Networks

Gómez-Villa et al. Journal of Neurophysiology 2019

The Wilson-Cowan interaction of wavelet-like visual neurons is analyzed in total correlation terms for the first time. Theoretical and empirical results show that a psychophysically-tuned interaction achieves the biggest efficiency in the most frequent region of the image space. This an original confirmation of the Efficient Coding Hypothesis and suggests that neural field models can be an alternative to Divisive Normalization in image compression.

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• Data and Code info_WilsonCowan.zip (70GB)

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Citation and References

FIRST PAPER:

A. Gómez-Villa, M. Bertalmío and J. Malo,
Visual information Flow in Wilson Cowan Networks. J. Neurophysiol. 123 (6): 2249-2268 (2020) https://doi.org/10.1152/jn.00487.2019

Related papers:

J. Malo.
Spatio-Chromatic Information available from different Neural Layers via Gaussianization
J. Mathematical Neuroscience (2020) https://doi.org/10.1186/s13408-020-00095-8
https://arxiv.org/abs/1910.01559 Web site

J. Malo.
Information Flow in Color Appearance Neural Networks arXiv: Quantitative Biology, Neurons and Cognition https://arxiv.org/abs/1912.12093 (2019)

Project Notebook:

J. Malo and Q. Li,
Visual information Flow in Psychophysical-Physiological networks. Notebook (as of July 2021)
Evolving Google Notebook