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Noise Correlations and Information Encoding and Decoding

  • Bruno B. Averbeck
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 3)

Abstract

Neuronal noise is correlated in the brain, and these correlations can affect both information encoding and decoding. In this chapter we discuss the recent progress that has been made, both theoretical and empirical, on how noise correlations affect information encoding and decoding. Specifically, we discuss theoretical results which show the conditions under which correlations either do or do not cause the amount of encoded information to saturate in modestly large populations of neurons. Correspondingly, we also describe the conditions under which information decoding can be affected by the presence of correlations. Complementing the theory, empirical studies have generally shown that the effects of correlations on both encoding and decoding are small in pairs of neurons. However, theory shows that small effects at the level of pairs of neurons can lead to large effects in populations. Thus, it is difficult to draw conclusions about the effects of correlations at the population level by studying pairs of neurons. Therefore, we conclude the chapter by briefly considering the issues around estimating information in larger populations.

Keywords

Classification Performance Fisher Information Neural Response Tuning Curve Noise Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Bruno B. Averbeck
    • 1
  1. 1.Sobell Department of Motor Neuroscience and Movement DisordersInstitute of Neurology, UCLLondonUK

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