Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Attention can either increase or decrease spike count correlations in visual cortex

Abstract

Visual attention enhances the responses of visual neurons that encode the attended location. Several recent studies have shown that attention also decreases correlations between fluctuations in the responses of pairs of neurons (termed spike count correlation or rSC). These results are consistent with two hypotheses. First, attention-related changes in rate and rSC might be linked (perhaps through a common mechanism), with attention always decreasing rSC. Second, attention might either increase or decrease rSC, possibly depending on the role of the neurons in the behavioral task. We recorded simultaneously from dozens of neurons in area V4 while monkeys performed a discrimination task. We found strong evidence in favor of the second hypothesis, showing that attention can flexibly increase or decrease correlations depending on whether the neurons provide evidence for the same or opposite choices. These results place important constraints on models of the neuronal mechanisms underlying cognitive factors.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Task and stimuli.
Figure 2: TTS calculation.
Figure 3: Hypotheses for how spike count correlations depend on TTS.
Figure 4: Attention can either increase or decrease spike count correlations.
Figure 5: Summary of results across 17 recording sessions.
Figure 6: The observed pattern of attention-related changes in rSC is not caused by differences in rate or baseline correlations.

References

  1. Cohen, M.R. & Kohn, A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 14, 811–819 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Maunsell, J.H.R. & Cook, E.P. The role of attention in visual processing. Phil. Trans. R. Soc. Lond. B 357, 1063–1072 (2002).

    Article  Google Scholar 

  3. Maunsell, J.H.R. & Treue, S. Feature-based attention in visual cortex. Trends Neurosci. 29, 317–322 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Reynolds, J.H. & Chelazzi, L. Attentional modulation of visual processing. Annu. Rev. Neurosci. 27, 611–647 (2004).

    Article  CAS  PubMed  Google Scholar 

  5. Yantis, S. & Serences, J.T. Cortical mechanisms of space-based and object-based attentional control. Curr. Opin. Neurobiol. 13, 187–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Cohen, M.R. & Maunsell, J.H.R. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12, 1594–1600 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Cohen, M.R. & Maunsell, J.H.R. Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron 70, 1192–1204 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mitchell, J.F., Sundberg, K.A. & Reynolds, J.H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63, 879–888 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Herrero, J.L., Gieselmann, M., Sanayei, M. & Thiele, A. Attention-induced variance and noise correlation reduction in macaque V1 is mediated by NMDA receptors. Neuron 78, 729–739 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zénon, A. & Krauzlis, R. Attention deficits without cortical neuronal deficits. Nature 489, 434–437 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Averbeck, B.B., Latham, P.E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Ecker, A.S., Berens, P., Tolias, A.S. & Bethge, M. The effect of noise correlations in populations of diversely tuned neurons. J. Neurosci. 31, 14272–14283 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Abbott, L.F. & Dayan, P. The effect of correlated variability on the accuracy of a population code. Neural Comput. 11, 91–101 (1999).

    Article  CAS  PubMed  Google Scholar 

  14. Zohary, E., Shadlen, M. & Newsome, W. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143 (1994).

    Article  CAS  PubMed  Google Scholar 

  15. Shadlen, M.N., Britten, K.H., Newsome, W.T. & Movshon, J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486–1510 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dean, A.F. The variability of discharge of simple cells in the cat striate cortex. Exp. Brain Res. 44, 437–440 (1981).

    Article  CAS  PubMed  Google Scholar 

  17. Albrecht, D.G. & Hamilton, D.B. Striate cortex of monkey and cat: function contrast response. J. Neurophysiol. 48, 217–237 (1982).

    Article  CAS  PubMed  Google Scholar 

  18. Sclar, G. & Freeman, R.D. Orientation selectivity in the cat's striate cortex is invariant with stimulus contrast. Exp. Brain Res. 46, 457–461 (1982).

    Article  CAS  PubMed  Google Scholar 

  19. Sclar, G., Maunsell, J. & Lennie, P. Coding of image contrast in central visual pathways of the macaque monkey. Vision Res. 30, 1–10 (1990).

    Article  CAS  PubMed  Google Scholar 

  20. Kohn, A. & Smith, M.A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25, 3661–3673 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Smith, M.A. & Kohn, A. Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28, 12591–12603 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gutnisky, D.A. & Dragoi, V. Adaptive coding of visual information in neural populations. Nature 452, 220–224 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Cohen, M.R. & Newsome, W.T. Context-dependent changes in functional circuitry in visual area MT. Neuron 60, 162–173 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ecker, A.S. et al. Decorrelated neuronal firing in cortical microcircuits. Science 327, 584–587 (2010).

    Article  CAS  PubMed  Google Scholar 

  25. Huang, X. & Lisberger, S.G. Noise correlations in cortical area MT and their potential impact on trial-by-trial variation in the direction and speed of smooth-pursuit eye movements. J. Neurophysiol. 101, 3012–3030 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Jermakowicz, W.J., Chen, X., Khaytin, I., Bonds, A. & Casagrande, V. Relationship between spontaneous and evoked spike-time correlations in primate visual cortex. J. Neurophysiol. 101, 2279–2289 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Smith, M.A., Jia, X., Zandvakili, A. & Kohn, A. Laminar dependence of neuronal correlations in visual cortex. J. Neurophysiol. 109, 940–947 (2013).

    Article  PubMed  Google Scholar 

  28. Hansen, B.J., Chelaru, M.I. & Dragoi, V. Correlated variability in laminar cortical circuits. Neuron 76, 590–602 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Smith, M.A. & Sommer, M.A. Spatial and temporal scales of neuronal correlation in visual area V4. J. Neurosci. 33, 5422–5432 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cohen, J.Y. et al. Cooperation and competition among frontal eye field neurons during visual target selection. J. Neurosci. 30, 3227–3238 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Leavitt, M.L., Pieper, F., Sachs, A., Joober, R. & Martinez-Trujillo, J.C. Structure of spike count correlations reveals functional interactions between neurons in dorsolateral prefrontal cortex area 8a of behaving primates. PLoS ONE 8, e61503 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Renart, A. et al. The asynchronous state in cortical circuits. Science 327, 587–590 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. de la Rocha, J., Doiron, B., Shea-Brown, E., Josić, K. & Reyes, A. A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Gu, Y. et al. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 71, 750–761 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Miura, K., Mainen, Z. & Uchida, N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74, 1087–1098 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jeanne, J.M., Sharpee, T.O. & Gentner, T.Q. Associative learning enhances population coding by inverting interneuronal correlation patterns. Neuron 78, 352–363 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. McAdams, C.J. & Maunsell, J.H.R. Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J. Neurosci. 19, 431–441 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Brainard, D.H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).

    Article  CAS  PubMed  Google Scholar 

  39. Pelli, D.G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10, 437–442 (1997).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Chang for assistance with animal training and recordings, K. McCracken for technical assistance, and J. Maunsell and B. Doiron for comments on an earlier version of this manuscript. The authors are supported by US National Institutes of Health grants 4R00EY020844-03 and R01 EY022930 (M.R.C.), a training grant slot on US National Institutes of Health grant 5T32NS7391-14 (D.A.R.), a Whitehall Foundation grant (M.R.C.), a Klingenstein Fellowship (M.R.C.) and a Sloan Research Fellowship (M.R.C.).

Author information

Authors and Affiliations

Authors

Contributions

D.A.R. and M.R.C. designed the experiments, analyzed the data and wrote the manuscript. D.A.R. conducted the experiments. M.R.C. supervised the project.

Corresponding author

Correspondence to Marlene R Cohen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Example recordings from chronically implanted microarrays

(a) An example recording session from Monkey F. Each panel shows overlaid waveforms from several seconds of threshold crossings from one electrode. The panels are arranged in the same spatial layout for the arrays implanted in the left hemisphere (8 rows by 6 columns on the left side of the figure) and right hemisphere (right side of the figure). (b) and (c) depict close up views of the waveforms from the electrodes highlighted in panel (a) in red and yellow, respectively. The trace in (b) contains a single-unit in addition to multi-unit activity. The trace in (c) contains multi-unit activity. (d) and (e) depict example PSTHs from a representative single unit and a representative multiunit cluster, respectively. The stimuli came on at time 0 and remained on the screen past the 300 ms mark where these plots end. Note the different Y–axis scale between (d) and (e) as well as the different baseline firing rates. The thin gray lines on either side of the black trace are s.e.m.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 166 kb)

Supplementary Methods Checklist

(PDF 362 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ruff, D., Cohen, M. Attention can either increase or decrease spike count correlations in visual cortex. Nat Neurosci 17, 1591–1597 (2014). https://doi.org/10.1038/nn.3835

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3835

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing