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Optimal decoding of correlated neural population responses in the primate visual cortex

Nature Neuroscience volume 9, pages 1412–1420 (2006)Cite this article

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Abstract

Even the simplest environmental stimuli elicit responses in large populations of neurons in early sensory cortical areas. How these distributed responses are read out by subsequent processing stages to mediate behavior remains unknown. Here we used voltage-sensitive dye imaging to measure directly population responses in the primary visual cortex (V1) of monkeys performing a demanding visual detection task. We then evaluated the ability of different decoding rules to detect the target from the measured neural responses. We found that small visual targets elicit widespread responses in V1, and that response variability at distant sites is highly correlated. These correlations render most previously proposed decoding rules inefficient relative to one that uses spatially antagonistic center-surround summation. This optimal decoder consistently outperformed the monkey in the detection task, demonstrating the sensitivity of our techniques. Overall, our results suggest an unexpected role for inhibitory mechanisms in efficient decoding of neural population responses.

NOTE: In the supplementary information initially published online to accompany this article,the “ ¢ ” symbols in Supplementary Figure 6 and Supplementary Methods were incorrectly placed in the equations. The correct symbol should be “ ' ”. The error has been corrected online.

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Figure 1: Behavioral task and recording chamber.
Figure 2: Neural population responses in V1 to a Gabor target (see Methods) measured with VSD imaging in one experiment.
Figure 3: Optimal two-site and multiple-site pooling.
Figure 4: Methods for measuring neural and behavioral detection sensitivity.
Figure 5: Comparing detection sensitivity of candidate pooling rules.
Figure 6: Effect of spatial binning on accuracy of the three pooling rules that rely on the response in a single site.
Figure 7: Effects of timing parameters on the accuracy of the seven pooling rules.

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  • 08 November 2006

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Notes

  1. In the supplementary information initially published online to accompany this article,the “ ¢ ” symbols in Supplementary Figure 6 and Supplementary Methods were incorrectly placed in the equations. The correct symbol should be “ ' ”. The error has been corrected online.

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Acknowledgements

We thank W. Bosking, D. Heeger, E. Kaplan, W. Newsome, R. Romo, M. Shadlen and H. Shouval for comments on earlier versions of this manuscript, C. Palmer, C. Michelson and Z. Yang for assistance with experiments and for discussions, and T. Cakic, C. Creeger, M. Hawthorne and M. Wu for technical support. This work was supported by the National Eye Institute, US National Institutes of Health and a Sloan Fellowship (to E.S.). W.S.G. was supported by the National Eye Institute, US National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Psychology and Center for Perceptual Systems, 1 University Station, A8000, University of Texas at Austin, Austin, 78712, Texas, USA

    Yuzhi Chen, Wilson S Geisler & Eyal Seidemann

Authors
  1. Yuzhi Chen
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  2. Wilson S Geisler
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  3. Eyal Seidemann
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Contributions

The research was conceived by E.S.; the data were collected by E.S. and Y.C.; all authors were involved in the data analysis, modeling and writing.

Note: Supplementary information is available on the Nature Neuroscience website.

Corresponding author

Correspondence to Eyal Seidemann.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Effect of contrast on response spread. (PDF 200 kb)

Supplementary Fig. 2

Effect of eye position on response spread. (PDF 218 kb)

Supplementary Fig. 3

Variability in V1 population responses, as measured by VSD imaging, can be described as an additive (stimulus independent) Gaussian noise with widespread spatial correlations. (PDF 252 kb)

Supplementary Fig. 4

Effect of pool size on correlations in population responses. (PDF 281 kb)

Supplementary Fig. 5

Comparison between spatial correlations measured from V1 and spatial correlations measured during control experiment with light emitting diode (LED) (see text). (PDF 239 kb)

Supplementary Fig. 6

Optimal two-point pooling in the example experiment. (PDF 175 kb)

Supplementary Methods (PDF 111 kb)

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Chen, Y., Geisler, W. & Seidemann, E. Optimal decoding of correlated neural population responses in the primate visual cortex. Nat Neurosci 9, 1412–1420 (2006). https://doi.org/10.1038/nn1792

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