Living Reference Work Entry

Encyclopedia of Computational Neuroscience

pp 1-6

Date: Latest Version

Applications of Information Theory to Analysis of Neural Data

  • Simon R. SchultzAffiliated withDepartment of Bioengineering, Imperial College London Email author 
  • , Robin A. A. InceAffiliated withInstitute of Neuroscience and Psychology, University of Glasgow
  • , Stefano PanzeriAffiliated withInstitute of Neuroscience and Psychology, University of GlasgowCenter for Neuroscience and Cognitive Systems, Italian Institute of Technology


Information theory is a practical and theoretical 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. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information-theoretic quantities are commonly used in neuroscience – (see entry “Definitions of Information-Theoretic Quantities”). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.

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