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  Applications of Information Theory to Analysis of Neural Data

Schultz, S., Ince, R., & Panzeri, S. (2015). Applications of Information Theory to Analysis of Neural Data. In D. Jaeger, & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 199-203). New York, NY, USA: Springer.

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 Creators:
Schultz, SR, Author
Ince, RAA, Author
Panzeri, S1, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: 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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 Dates: 2015
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-1-4614-7320-6_280-1
BibTex Citekey: SchultzIP2014
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Title: Encyclopedia of Computational Neuroscience
Source Genre: Book
 Creator(s):
Jaeger, D., Editor
Jung, R., Editor
Affiliations:
-
Publ. Info: New York, NY, USA : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 199 - 203 Identifier: ISBN: 978-1-4614-6674-1