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.

Decoding the activity of neuronal populations in macaque primary visual cortex

This article has been updated

Abstract

Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for sensory decoding. These results show how neuronal responses can best be used to inform perceptual decision-making.

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
Figure 2: Scheme of the linear log-likelihood decoding framework with parameters of the ELD derived from data set 3.
Figure 3: Orientation estimation accuracy for the ELD, the CB-ELD and the PID.
Figure 4: Orientation discrimination accuracy for the ELD, the CB-ELD and the PID.
Figure 5: Dependency of the discrimination weighting functions with respect to stimulus orientation and neuronal response magnitude for data set 3.
Figure 6: Orientation discrimination thresholds of the ELD corresponding to an accuracy of 0.75 using data set 3.

Change history

  • 16 January 2011

    In the version of this article initially published online, an error was made in the legend for Figure 6. In the legend, 0.75% should read 0.75. This error has been corrected for the print, PDF and HTML versions of this article.

References

  1. Pouget, A., Dayan, P. & Zemel, R.S. Inference and computation with population codes. Annu. Rev. Neurosci. 26, 381–410 (2003).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Newsome, W.T., Britten, K.H. & Movshon, J.A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).

    Article  CAS  Google Scholar 

  4. Oram, M.W., Foldiak, P., Perrett, D.I. & Sengpiel, F. The 'Ideal Homunculus': decoding neural population signals. Trends Neurosci. 21, 259–265 (1998).

    Article  CAS  Google Scholar 

  5. Sanger, T.D. Probability density estimation for the interpretation of neural population codes. J. Neurophysiol. 76, 2790–2793 (1996).

    Article  CAS  Google Scholar 

  6. Foldiak, P. The “Ideal Homunculus”: statistical inference from neural population responses. in Computation and Neural Systems (eds. Eeckman, F.H. & Bower, J.M.) 55–60 (Kluwer Academic, 1993).

  7. Bradley, A., Skottun, B.C., Ohzawa, I., Sclar, G. & Freeman, R.D. Visual orientation and spatial frequency discrimination: a comparison of single neurons and behavior. J. Neurophysiol. 57, 755–772 (1987).

    Article  CAS  Google Scholar 

  8. Geisler, W.S. & Albrecht, D.G. Visual cortex neurons in monkeys and cats: detection, discrimination, and identification. Vis. Neurosci. 14, 897–919 (1997).

    Article  CAS  Google Scholar 

  9. Tolhurst, D.J., Movshon, J.A. & Dean, A.F. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res. 23, 775–785 (1983).

    Article  CAS  Google Scholar 

  10. Vogels, R. & Orban, G.A. How well do response changes of striate neurons signal differences in orientation: a study in the discriminating monkey. J. Neurosci. 10, 3543–3558 (1990).

    Article  CAS  Google Scholar 

  11. Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity (Springer Verlag, 1998).

  12. Britten, K.H., Newsome, W.T., Shadlen, M.N., Celebrini, S. & Movshon, J.A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).

    Article  CAS  Google Scholar 

  13. 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  Google Scholar 

  14. 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  Google Scholar 

  15. Butts, D.A. & Goldman, M.S. Tuning curves, neuronal variability, and sensory coding. PLoS Biol. 4, e92 (2006).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  17. Montani, F., Kohn, A., Smith, M.A. & Schultz, S.R. The role of correlations in direction and contrast coding in the primary visual cortex. J. Neurosci. 27, 2338–2348 (2007).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Deneve, S., Latham, P.E. & Pouget, A. Reading population codes: a neural implementation of ideal observers. Nat. Neurosci. 2, 740–745 (1999).

    Article  CAS  Google Scholar 

  20. Shamir, M. & Sompolinsky, H. Implications of neuronal diversity on population coding. Neural Comput. 18, 1951–1986 (2006).

    Article  Google Scholar 

  21. Georgopoulos, A.P., Schwartz, A.B. & Kettner, R.E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    Article  CAS  Google Scholar 

  22. Salinas, E. & Abbott, L.F. Vector reconstruction from firing rates. J. Comput. Neurosci. 1, 89–107 (1994).

    Article  CAS  Google Scholar 

  23. Dayan, P. & Abbott, L.F. Theoretical Neuroscience (MIT Press, 2001).

  24. Seriès, P., Latham, P.E. & Pouget, A. Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat. Neurosci. 7, 1129–1135 (2004).

    Article  Google Scholar 

  25. Jazayeri, M. & Movshon, J.A. Optimal representation of sensory information by neural populations. Nat. Neurosci. 9, 690–696 (2006).

    Article  CAS  Google Scholar 

  26. Ma, W.J., Beck, J.M., Latham, P.E. & Pouget, A. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9, 1432–1438 (2006).

    Article  CAS  Google Scholar 

  27. Jaynes, E.T. Probability Theory: The Logic of Science (Cambridge University Press, 2003).

  28. Seung, H.S. & Sompolinsky, H. Simple models for reading neuronal population codes. Proc. Natl. Acad. Sci. USA 90, 10749–10753 (1993).

    Article  CAS  Google Scholar 

  29. Simoncelli, E. Optimal estimation in sensory systems. in The Cognitive Neurosciences (MIT Press, 2009).

  30. Beverley, K.I. & Regan, D. The relation between discrimination and sensitivity in the perception of motion in depth. J. Physiol. (Lond.) 249, 387–398 (1975).

    Article  CAS  Google Scholar 

  31. Jazayeri, M. & Movshon, J.A. A new perceptual illusion reveals mechanisms of sensory decoding. Nature 446, 912–915 (2007).

    Article  CAS  Google Scholar 

  32. Ernst, M.O. & Banks, M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    Article  CAS  Google Scholar 

  33. Tassinari, H., Hudson, T.E. & Landy, M.S. Combining priors and noisy visual cues in a rapid pointing task. J. Neurosci. 26, 10154–10163 (2006).

    Article  CAS  Google Scholar 

  34. Yang, T. & Shadlen, M.N. Probabilistic reasoning by neurons. Nature 447, 1075–1080 (2007).

    Article  CAS  Google Scholar 

  35. Zhang, K., Ginzburg, I., McNaughton, B.L. & Sejnowski, T.J. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79, 1017–1044 (1998).

    Article  CAS  Google Scholar 

  36. Averbeck, B.B. & Lee, D. Effects of noise correlations on information encoding and decoding. J. Neurophysiol. 95, 3633–3644 (2006).

    Article  Google Scholar 

  37. Benucci, A., Ringach, D.L. & Carandini, M. Coding of stimulus sequences by population responses in visual cortex. Nat. Neurosci. 12, 1317–1324 (2009).

    Article  CAS  Google Scholar 

  38. Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649–657 (2003).

    Article  CAS  Google Scholar 

  39. Pillow, J.W. et al. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 454, 995–999 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  41. Wu, S., Nakahara, H. & Amari, S. Population coding with correlation and an unfaithful model. Neural Comput. 13, 775–797 (2001).

    Article  CAS  Google Scholar 

  42. Nirenberg, S., Carcieri, S.M., Jacobs, A.L. & Latham, P.E. Retinal ganglion cells act largely as independent encoders. Nature 411, 698–701 (2001).

    Article  CAS  Google Scholar 

  43. Beck, J.M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).

    Article  CAS  Google Scholar 

  44. Schölkopf, B. & Smola, A. Learning with Kernels (MIT Press, 2002).

  45. Vapnik, V. The Nature of Statistical Learning Theory (Springer, 2000).

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

    Article  CAS  Google Scholar 

  47. Carandini, M., Heeger, D.J. & Movshon, J.A. Linearity and normalization in simple cells of the macaque primary visual cortex. J. Neurosci. 17, 8621–8644 (1997).

    Article  CAS  Google Scholar 

  48. Law, C.T. & Gold, J.I. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11, 505–513 (2008).

    Article  CAS  Google Scholar 

  49. Bair, W., Cavanaugh, J.R., Smith, M.A. & Movshon, J.A. The timing of response onset and offset in macaque visual neurons. J. Neurosci. 22, 3189–3205 (2002).

    Article  CAS  Google Scholar 

  50. Schmolesky, M.T. et al. Signal timing across the macaque visual system. J. Neurophysiol. 79, 3272–3278 (1998).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to M. Smith and R. Kelly for their help with recording and to E. Simoncelli and M. Yanike for helpful comments on the manuscript. This research was supported by US National Institutes of Health research grants EY2017 and EY4440, training grant EY7158 and the Swartz Foundation.

Author information

Authors and Affiliations

Authors

Contributions

A.B.A.G., A.K. and J.A.M. designed the experiments, A.B.A.G. and A.K. collected the data, A.B.A.G. created the models and analyzed the data, A.B.A.G. and J.A.M. wrote the manuscript, and A.K. and M.J. contributed to the intellectual development of the project and to the writing of the manuscript.

Corresponding author

Correspondence to Arnulf B A Graf.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 2190 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Graf, A., Kohn, A., Jazayeri, M. et al. Decoding the activity of neuronal populations in macaque primary visual cortex. Nat Neurosci 14, 239–245 (2011). https://doi.org/10.1038/nn.2733

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.2733

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