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Mutual Information, Fisher Information, and Efficient Coding.

Xue-Xin Wei1, Alan A Stocker2

  • 1Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, U.S.A. weixxpku@gmail.com.

Neural Computation
|December 15, 2015
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Summary
This summary is machine-generated.

Fisher information is not always a lower bound for mutual information in neural coding. Our findings suggest Fisher information is a better approximation, offering new insights into efficient neural coding.

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Area of Science:

  • Computational neuroscience
  • Information theory
  • Neural coding

Background:

  • Mutual information quantifies information transmission in neural systems.
  • Fisher information is commonly used as a lower bound for mutual information to assess neural coding efficiency.

Purpose of the Study:

  • To re-evaluate the relationship between Fisher information and mutual information.
  • To provide a more accurate framework for understanding neural coding efficiency.

Main Methods:

  • Analytical derivation of the relationship between Fisher information and mutual information.
  • Analysis of the small noise regime in neural coding.
  • Formulation of the efficient coding problem using Fisher information.

Main Results:

  • Fisher information can act as an upper bound for mutual information under certain conditions (e.g., small noise).
  • Established conditions for when Fisher information serves as a good approximation of mutual information.
  • Derived a novel signature of efficient coding based on population Fisher information and stimulus distribution.

Conclusions:

  • Fisher information is more accurately viewed as an approximation, not a strict bound, of mutual information.
  • The derived signature of efficient coding explains observed neural tuning characteristics, improving upon previous models.
  • Provides a more general framework for understanding efficient coding in neural populations.