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  The effect of noise correlations in populations of diversely tuned neurons

Ecker, A., Berens, P., Tolias, A., & Bethge, M. (2011). The effect of noise correlations in populations of diversely tuned neurons. The Journal of Neuroscience, 31(40), 14272-14283. doi:10.1523/​JNEUROSCI.2539-11.2011.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B994-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B046-9
Genre: Journal Article

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 Creators:
Ecker, AS1, 2, Author              
Berens, P1, 2, Author              
Tolias, AS, Author              
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations.

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 Dates: 2011-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1523/​JNEUROSCI.2539-11.2011
BibTex Citekey: EckerBTB2011
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Title: The Journal of Neuroscience
  Other : The Journal of Neuroscience: the Official Journal of the Society for Neuroscience
  Abbreviation : J. Neurosci.
Source Genre: Journal
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Publ. Info: Washington, DC : Society of Neuroscience
Pages: - Volume / Issue: 31 (40) Sequence Number: - Start / End Page: 14272 - 14283 Identifier: ISSN: 0270-6474
CoNE: /journals/resource/954925502187_1