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  Reassessing optimal neural population codes with neurometric functions

Berens, P., Ecker, A., Gerwinn, S., Tolias, A., & Bethge, M. (2011). Reassessing optimal neural population codes with neurometric functions. Proceedings of the National Academy of Sciences of the United States of America, 108(11), 4423-4428. doi:10.1073/pnas.1015904108.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BC66-F Version Permalink: http://hdl.handle.net/21.11116/0000-0001-BC2D-A
Genre: Journal Article

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 Creators:
Berens, P1, 2, Author              
Ecker, AS1, 2, Author              
Gerwinn, S1, 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: Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool—Fisher information—can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and a reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information.

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 Dates: 2011-03
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1073/pnas.1015904108
BibTex Citekey: BerensEGTB2011
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Title: Proceedings of the National Academy of Sciences of the United States of America
  Other : Proceedings of the National Academy of Sciences of the USA
  Other : Proc. Acad. Sci. USA
  Other : Proc. Acad. Sci. U.S.A.
  Abbreviation : PNAS
Source Genre: Journal
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Publ. Info: Washington, D.C. : National Academy of Sciences
Pages: - Volume / Issue: 108 (11) Sequence Number: - Start / End Page: 4423 - 4428 Identifier: ISSN: 0027-8424
CoNE: /journals/resource/954925427230