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Decoding neuronal spike trains: how important are correlations?

Sheila Nirenberg1, Peter E Latham

  • 1Department of Neurobiology, University of California, Los Angeles, CA 90095-1763, USA. sheilan@ucla.edu

Proceedings of the National Academy of Sciences of the United States of America
|May 31, 2003
PubMed
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Neuronal spike train correlations may carry unique information. This study introduces a novel information-theoretic framework to quantify the information lost when ignoring these correlations, aiding analysis of the neural code.

Area of Science:

  • Computational Neuroscience
  • Neural Coding
  • Information Theory

Background:

  • Neuronal spike trains exhibit correlations, suggesting a role in neural communication.
  • The potential of spike train correlations to convey information beyond firing rate is debated.
  • Separating correlation-based information from other neural signals remains a challenge.

Purpose of the Study:

  • To propose a framework for distinguishing information encoded in neuronal correlations.
  • To develop a method to quantify the impact of ignoring spike train correlations on information decoding.

Main Methods:

  • Derivation of an information-theoretic cost function.
  • The cost function measures the difficulty in decoding neuronal responses when correlations are disregarded.

Related Experiment Videos

  • The framework is designed for application to real neuronal data.

    • Main Results:

    • A quantifiable measure is introduced to assess the information content of neuronal correlations.
    • The proposed cost function provides a means to evaluate the significance of correlations in neural coding.

    Conclusions:

    • The developed framework offers a solution for separating information carried by spike train correlations.
    • This approach can help resolve the debate on the role of correlations in the neural code.
    • The method is practical for analyzing empirical neuroscience data.