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.

  • Article
  • Published:

Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations

Nature Neuroscience volume 7, pages 1129–1135 (2004)Cite this article

Abstract

Several studies have shown that the information conveyed by bell-shaped tuning curves increases as their width decreases, leading to the notion that sharpening of tuning curves improves population codes. This notion, however, is based on assumptions that the noise distribution is independent among neurons and independent of the tuning curve width. Here we reexamine these assumptions in networks of spiking neurons by using orientation selectivity as an example. We compare two principal classes of model: one in which the tuning curves are sharpened through cortical lateral interactions, and one in which they are not. We report that sharpening through lateral interactions does not improve population codes but, on the contrary, leads to a severe loss of information. In addition, the sharpening models generate complicated codes that rely extensively on pairwise correlations. Our study generates several experimental predictions that can be used to distinguish between these two classes of model.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Properties of V1 neurons in response to a bar oriented at 90 deg.
Figure 2: Information comparison across models.
Figure 3: Covariance matrices of the V1 cells in both models.
Figure 4: Shuffled information.
Figure 5: Information (ILOLE and Idiag) in the output layer of additional models.

Similar content being viewed by others

References

  1. Paradiso, M. A theory of the use of visual orientation information which exploits the columnar structure of striate cortex. Biol. Cybern. 58, 35–49 (1988).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Somers, D.C., Nelson, S.B. & Sur, M. An emergent model of orientation selectivity in cat visual cortical simple cells. J. Neurosci. 15, 5448–5465 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Spitzer, H., Desimone, R. & Moran, J. Increased attention enhances both behavioral and neuronal performance. Science 240, 338–340 (1988).

    Article  CAS  PubMed  Google Scholar 

  5. Lee, D.K., Itti, L., Koch, C. & Braun, J. Attention activates winner-take-all competition among visual filters. Nat. Neurosci. 2, 375–381 (1999).

    Article  CAS  PubMed  Google Scholar 

  6. Murray, S.O. & Wojciulik, E. Attention increases neural selectivity in the human lateral occipital complex. Nat. Neurosci. 7, 70–74 (2004).

    Article  CAS  PubMed  Google Scholar 

  7. Schoups, A., Vogels, R., Qian, N. & Orban, G. Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549–553 (2001).

    Article  CAS  PubMed  Google Scholar 

  8. Teich, A.F. & Qian, N. Learning and adaptation in a recurrent model of V1 orientation selectivity. J. Neurophysiol. 89, 2086–2100 (2003).

    Article  PubMed  Google Scholar 

  9. Yang, T. & Maunsell, J.H. The effect of perceptual learning on neuronal responses in monkey visual area V4. J. Neurosci. 24, 1617–1626 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Fitzpatrick, D.C., Batra, R., Stanford, T.R. & Kuwada, S. A neuronal population code for sound localization. Nature 388, 871–874 (1997).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  12. Lee, D., Port, N.L., Kruse, W. & Georgopoulos, A.P. Variability and correlated noise in the discharge of neurons in motor and parietal areas of the primate cortex. J. Neurosci. 18, 1161–1170 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hubel, D. & Wiesel, T. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. (Lond.) 160, 106–154 (1962).

    Article  CAS  Google Scholar 

  14. Ferster, D. & Miller, K.D. Neural mechanisms of orientation selectivity in the visual cortex. Annu. Rev. Neurosci. 23, 441–471 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Monier, C., Chavane, F., Baudot, P., Graham, L.J. & Frégnac, Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. Neuron 37, 663–680 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. Shapley, R., Hawken, M. & Ringach, D.L. Dynamics of orientation selectivity in the primary visual cortex. Neuron 38, 689–699 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Sompolinsky, H. & Shapley, R. New perspectives on the mechanisms for orientation selectivity. Curr. Opin. Neurobiol. 7, 514–522 (1997).

    Article  CAS  PubMed  Google Scholar 

  18. Ben-Yishai, R., Bar-Or, R.L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl. Acad. Sci. USA 92, 3844–3848 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Troyer, T.W., Krukowski, A.E., Priebe, N.J. & Miller, K.D. Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J. Neurosci. 18, 5908–5927 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. McLaughlin, D., Shapley, R., Shelley, M. & Wielaard, D.J. A neuronal network model of macaque primary visual cortex (V1): orientation selectivity and dynamics in the input layer 4Cα. Proc. Natl. Acad. Sci. USA 97, 8087–8092 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sclar, G. & Freeman, R. 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 

  22. Gershon, E.D., Wiener, M.C., Latham, P.E. & Richmond, B.J. Coding strategies in monkey V1 and inferior temporal cortices. J. Neurophysiol. 79, 1135–1144 (1998).

    Article  CAS  PubMed  Google Scholar 

  23. Softky, W.R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pouget, A., Zhang, K., Deneve, S. & Latham, P.E. Statistically efficient estimation using population codes. Neural Comput. 10, 373–401 (1998).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  26. Yoon, H. & Sompolinsky, H. The effect of correlations on the Fisher Information of Populations codes. in Advances in Neural Information Processing Systems (eds. Kearns, M. S., Solla, S. & Cohn, D. A.) 167–173 (MIT Press, Cambridge, Massachusetts, USA, 1999).

    Google Scholar 

  27. Abbott, L. & 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 

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

    Article  CAS  PubMed  Google Scholar 

  29. Nirenberg, S. & Latham, P.E. Decoding neuronal spike trains: how important are correlations? Proc. Natl. Acad. Sci. USA 100, 7348–7353 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jordan, M.I., Ghahramani, Z., Jaakkola, T.S. & Saul, L.K. An introduction to variational methods for graphical models. Mach. Learn. 37, 183–233 (1999).

    Article  Google Scholar 

  31. Latham, P.E., Deneve, S. & Pouget, A. Optimal computation with attractor networks. J. Physiol. (Paris) 97, 683–694 (2003).

    Article  Google Scholar 

  32. Cover, T.M. & Thomas, J.A. Elements of Information Theory (John Wiley & Sons, New York, 1991).

    Book  Google Scholar 

  33. Shamir, M. & Sompolinsky, H. Nonlinear population codes. Neural Comput. 16, 1105–1136 (2004).

    Article  PubMed  Google Scholar 

  34. Simoncelli, E.P. & Olshausen, B.A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

    Article  CAS  PubMed  Google Scholar 

  35. Zucker, S.W., Dobbins, A. & Iverson, L. Two stages of curve detection suggest two styles of visual computation. Neural Comput. 1, 68–81 (1989).

    Article  Google Scholar 

  36. Li, Z. A neural model of contour integration in the primary visual cortex. Neural Comput. 10, 903–940 (1998).

    Article  CAS  PubMed  Google Scholar 

  37. Hess, R. & Field, D. Integration of contours: new insights. Trends Cogn. Sci. 3, 480–486 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Seriès, P., Lorenceau, J. & Frégnac, Y. The silent surround of V1 receptive fields: theory and experiments. J. Physiol. (Paris) 97, 453–474 (2004).

    Article  Google Scholar 

  39. Spiridon, M. & Gerstner, W. Effect of lateral connections on the accuracy of the population code for a network of spiking neurons. Network 12, 409–421 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Rolls, E.T., Franco, L., Aggelopoulos, N.C. & Reece, S. An information theoretic approach to the contributions of the firing rates and the correlations between the firing of neurons. J. Neurophysiol. 89, 2810–2822 (2003).

    Article  PubMed  Google Scholar 

  41. 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  PubMed  Google Scholar 

  42. Averbeck, B.B. & Lee, D. Neural noise and movement-related codes in the macaque supplementary motor area. J. Neurosci. 23, 7630–7641 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Panzeri, S., Petroni, F., Petersen, R.S. & Diamond, M.E. Decoding neuronal population activity in rat somatosensory cortex: role of columnar organization. Cereb. Cortex 13, 45–52 (2003).

    Article  PubMed  Google Scholar 

  44. Deneve, S., Latham, P. & Pouget, A. Efficient computation and cue integration with noisy population codes. Nat. Neurosci. 4, 826–831 (2001).

    Article  CAS  PubMed  Google Scholar 

  45. Green, D.M. & Swets, J.A. Signal Detection Theory and Psychophysics (John Wiley & Sons, Los Altos, California, USA, 1966).

    Google Scholar 

  46. Duda, R.O. & Hart, P.E. Pattern Classification and Scene Analysis (John Wiley & Sons, New York, 1973).

    Google Scholar 

  47. Rumelhart, D., Hinton, G. & Williams, R. Learning internal representation by error propagation. in Parallel Distributed Processing (eds. Rumelhart, D., McClelland, J. & Group, P.R.) 318–362 (MIT Press, Cambridge, Massachusetts, USA, 1986).

    Google Scholar 

  48. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    Google Scholar 

Download references

Acknowledgements

P.S. and A.P. were supported by a grant from the Office of Naval Research (N00014-00-1-0642) and a fellowship from the McDonnell-Pew foundation. P.L. was supported by a grant from the National Institute of Mental Health (R01 MH62447).

Author information

Authors and Affiliations

  1. Gatsby Computational Neuroscience Unit, Alexandra House, 17 Queen Square, London, WC1N 3AR, UK

    Peggy Seriès & Peter E Latham

  2. Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA

    Alexandre Pouget

Authors
  1. Peggy Seriès
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter E Latham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandre Pouget
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexandre Pouget.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Seriès, P., Latham, P. & Pouget, A. Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat Neurosci 7, 1129–1135 (2004). https://doi.org/10.1038/nn1321

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1321

This article is cited by

Search

Advanced 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