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Vision

Vision

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.

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Related Experiment Video

Updated: Jan 11, 2026

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Published on: February 8, 2020

Information-theoretic gradient flows in mouse visual cortex.

Erik D Fagerholm¹, Hirokazu Tanaka², Milan Brázdil¹

¹First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czechia.

Frontiers in Neuroinformatics

|November 17, 2025

Summary

This summary is machine-generated.

Researchers developed a new mathematical framework to analyze how neural activity transforms between brain regions. This method quantifies information flow by measuring changes in entropy and expectation, offering insights into brain organization.

Keywords:

calcium imaging entropy expectation gradient flows information geometry neural connectivity two photon

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Area of Science:

Computational neuroscience
Information theory
Neuroimaging analysis

Background:

Neural activity is characterized by evolving probability distributions.
Understanding transformations of these distributions between cortical regions is crucial for deciphering brain information processing.

Purpose of the Study:

To develop a mathematical framework for quantifying neural signal transformations.
To interpret these transformations using information-theoretic concepts.

Main Methods:

Developed information-theoretic gradient flows to model neural probability distribution changes.
Applied the framework to calcium imaging data from the mouse visual cortex.
Validated the framework *in silico*.

Main Results:

Identified consistent bi-directional information flow between the rostrolateral area and primary visual cortex in mice.
Demonstrated that entropy and expectation contributions can be disambiguated to describe information flow.
Showcased the framework's ability to analyze neural probability distribution dynamics.

Conclusions:

Introduced a generalizable framework for decomposing neural signal transformations.
The method provides interpretable, information-theoretic components for analyzing cortical communication.
Applicable to diverse neuroimaging modalities and scales for understanding information geometry.