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The Gabor function extracts the maximum information from input local signals.

¹Exploratory Research Laboratory, Fundamental Research Laboratories, NEC Corporation, Tsukuba, Japan

Neural Networks : the Official Journal of the International Neural Network Society

|March 29, 2003

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Simple cell receptive fields in the visual cortex, approximated by Gabor functions, are derived from maximizing mutual information. This suggests optimal information extraction from visual signals, supporting an information-theoretic design for these cells.

Area of Science:

Neuroscience
Computational Neuroscience
Information Theory

Background:

Simple cells in the visual cortex exhibit receptive fields well-modeled by Gabor functions.
Understanding the functional principles behind these receptive fields is crucial for visual neuroscience.

Purpose of the Study:

To demonstrate that Gabor functions, which approximate simple cell receptive fields, can be derived from a mutual information maximization principle.
To provide an information-theoretic explanation for the structure of simple cell receptive fields.

Main Methods:

Deriving Gabor functions as solutions to a mutual information maximization problem.
Analyzing the implications of this derivation under general assumptions about input signals.

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Main Results:

Gabor functions emerge as optimal solutions for extracting maximum information from local input signals.
This derivation holds under broad assumptions, indicating robustness.

Conclusions:

The receptive fields of simple cells in the visual cortex appear to be optimally designed from an information-theoretic perspective.
Mutual information maximization provides a theoretical framework for understanding the emergence of Gabor-like receptive fields.