Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Summary of Information Theoretic Quantities

  • Robin A. A. Ince
  • Stefano Panzeri
  • Simon R. Schultz
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_306-1

Definition

Information theory is a practical and theoretic framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. As a framework, it has a number of useful properties: it provides a general measure sensitive to any relationship, not only linear effects; its quantities have meaningful units which, in many cases, allow a direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single experimental trials rather than in averages over multiple trials. A variety of information theoretic quantities are in common use in neuroscience – including the Shannon entropy, Kullback–Leibler divergence, and mutual information. In this entry, we introduce and define these quantities. Further details on how these quantities can be estimated in...

Keywords

Entropy 
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Notes

Acknowledgements

Research supported by the SI-CODE (FET-Open, FP7-284533) project and by the ABC and NETT (People Programme Marie Curie Actions PITN-GA-2011-290011 and PITN-GA-2011-289146) projects of the European Union’s Seventh Framework Programme FP7 2007–2013.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Robin A. A. Ince
    • 1
  • Stefano Panzeri
    • 1
    • 2
  • Simon R. Schultz
    • 3
  1. 1.School of Psychology, Institute of Neuroscience and PsychologyUniversity of GlasgowGlasgowUK
  2. 2.Center for Neuroscience and Cognitive SystemsItalian Institute of TechnologyRoveretoItaly
  3. 3.Department of BioengineeringImperial College LondonLondonUK