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  Information theory and neural coding

Borst, A., & Theunissen, F. E. (1999). Information theory and neural coding. Nature Neuroscience, 2(11), 947-957. doi:10.1038/14731.

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 Creators:
Borst, Alexander, Author
Theunissen, Frederic E.1, Author           
Affiliations:
1University Berkeley, USA, ou_persistent22              

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Free keywords: Algorithms Animals Entropy Humans *Information Theory Linear Models Nerve Net/*physiology Neurons/physiology Probability Reproducibility of Results
 Abstract: Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis. Here we review information-theory basics before demonstrating its use in neural coding. We show how to use information theory to validate simple stimulus-response models of neural coding of dynamic stimuli. Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding. This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission. We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information.

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Language(s): eng - English
 Dates: 1999
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/14731
 Degree: -

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Title: Nature Neuroscience
  Other : Nat. Neurosci.
Source Genre: Journal
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Affiliations:
Publ. Info: New York, NY : Nature America Inc.
Pages: - Volume / Issue: 2 (11) Sequence Number: - Start / End Page: 947 - 957 Identifier: ISSN: 1097-6256
CoNE: https://pure.mpg.de/cone/journals/resource/954925610931