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.

Perceptual training continuously refines neuronal population codes in primary visual cortex

Subjects

Abstract

Perceptual learning substantially improves visual discrimination and detection ability, which has been associated with visual cortical plasticity. However, little is known about the dynamic changes in neuronal response properties over the course of training. Using chronically implanted multielectrode arrays, we were able to capture day-by-day spatiotemporal dynamics of neurons in the primary visual cortex (V1) of monkeys trained to detect camouflaged visual contours. We found progressive strengthening and accelerating in both facilitation of neurons encoding the contour elements and suppression of neurons responding to the background components. The enhancement of this figure-ground contrast in V1 was closely correlated with improved behavioral performance on a daily basis. Decoding accuracy of a simple linear classifier based on V1 population responses also paralleled the animal's behavioral changes. Our results indicate that perceptual learning shapes the V1 population code to allow a more efficient readout of task-relevant information.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Experimental design.
Figure 2: Contour-related signals in V1 and their changes with training.
Figure 3: Quantification of learning-induced changes in V1 for the learned conditions.
Figure 4: Learning-induced changes in temporal dynamics of V1 responses.
Figure 5: Decoding V1 population responses.
Figure 6: Further decoding analyses.

References

  1. Sasaki, Y., Nanez, J.E. & Watanabe, T. Advances in visual perceptual learning and plasticity. Nat. Rev. Neurosci. 11, 53–60 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Sagi, D. Perceptual learning in vision research. Vision Res. 51, 1552–1566 (2011).

    Article  PubMed  Google Scholar 

  3. Gilbert, C.D. & Li, W. Adult visual cortical plasticity. Neuron 75, 250–264 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. Crist, R.E., Li, W. & Gilbert, C.D. Learning to see: Experience and attention in primary visual cortex. Nat. Neurosci. 4, 519–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  6. Li, W., Piech, V. & Gilbert, C.D. Learning to link visual contours. Neuron 57, 442–451 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hua, T. et al. Perceptual learning improves contrast sensitivity of V1 neurons in cats. Curr. Biol. 20, 887–894 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rainer, G., Lee, H. & Logothetis, N.K. The effects of learning on the function of monkey extrastriate visual cortex. PLoS Biol. 2, e44 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Raiguel, S., Vogels, R., Mysore, S.G. & Orban, G.A. Learning to see the difference specifically alters the most informative V4 neurons. J. Neurosci. 26, 6589–6602 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Adab, H.Z. & Vogels, R. Practicing coarse orientation discrimination improves orientation signals in macaque cortical area V4. Curr. Biol. 21, 1661–1666 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Bartolucci, M. & Smith, A.T. Attentional modulation in visual cortex is modified during perceptual learning. Neuropsychologia 49, 3898–3907 (2011).

    Article  PubMed  Google Scholar 

  13. Petrov, A.A., Dosher, B.A. & Lu, Z.L. The dynamics of perceptual learning: An incremental reweighting model. Psychol. Rev. 112, 715–743 (2005).

    Article  PubMed  Google Scholar 

  14. 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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang, G.L., Cong, L.J., Song, Y. & Yu, C. ERP P1-N1 changes associated with Vernier perceptual learning and its location specificity and transfer. J. Vis. 13, 19 (2013).

    Article  PubMed  Google Scholar 

  16. Schwartz, S., Maquet, P. & Frith, C. Neural correlates of perceptual learning: a functional MRI study of visual texture discrimination. Proc. Natl. Acad. Sci. USA [comment] 99, 17137–17142 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Furmanski, C.S., Schluppeck, D. & Engel, S.A. Learning strengthens the response of primary visual cortex to simple patterns. Curr. Biol. 14, 573–578 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Kourtzi, Z., Betts, L.R., Sarkheil, P. & Welchman, A.E. Distributed neural plasticity for shape learning in the human visual cortex. PLoS Biol. 3, e204 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sigman, M. et al. Top-down reorganization of activity in the visual pathway after learning a shape identification task. Neuron 46, 823–835 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Schiltz, C. et al. Neuronal mechanisms of perceptual learning: changes in human brain activity with training in orientation discrimination. Neuroimage 9, 46–62 (1999).

    Article  CAS  PubMed  Google Scholar 

  21. Mukai, I. et al. Activations in visual and attention-related areas predict and correlate with the degree of perceptual learning. J. Neurosci. 27, 11401–11411 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yotsumoto, Y., Watanabe, T. & Sasaki, Y. Different dynamics of performance and brain activation in the time course of perceptual learning. Neuron 57, 827–833 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jehee, J.F.M., Ling, S., Swisher, J.D., van Bergen, R.S. & Tong, F. Perceptual learning selectively refines orientation representations in early visual cortex. J. Neurosci. 32, 16747–16753 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li, W., Piech, V. & Gilbert, C.D. Contour saliency in primary visual cortex. Neuron 50, 951–962 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Li, W. & Gilbert, C.D. Global contour saliency and local colinear interactions. J. Neurophysiol. 88, 2846–2856 (2002).

    Article  PubMed  Google Scholar 

  26. Nugent, A.K., Keswani, R.N., Woods, R.L. & Peli, E. Contour integration in peripheral vision reduces gradually with eccentricity. Vision Res. 43, 2427–2437 (2003).

    Article  PubMed  Google Scholar 

  27. Chen, M. et al. Incremental integration of global contours through interplay between visual cortical areas. Neuron 82, 682–694 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Li, W., Piech, V. & Gilbert, C.D. Perceptual learning and top-down influences in primary visual cortex. Nat. Neurosci. 7, 651–657 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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 

  30. Rosenblatt, F. The perceptron: a perceiving and recognizing automaton (Report 85–460–1) (Cornell Aeronautical Laboratory, 1957).

  31. Maass, W., Natschlager, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002).

    Article  PubMed  Google Scholar 

  32. Bauer, R. & Heinze, S. Contour integration in striate cortex. Classic cell responses or cooperative selection? Exp. Brain Res. 147, 145–152 (2002).

    Article  PubMed  Google Scholar 

  33. McManus, J.N.J., Li, W. & Gilbert, C.D. Adaptive shape processing in primary visual cortex. Proc. Natl. Acad. Sci. USA 108, 9739–9746 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zipser, K., Lamme, V.A. & Schiller, P.H. Contextual modulation in primary visual cortex. J. Neurosci. 16, 7376–7389 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Roelfsema, P.R., Tolboom, M. & Khayat, P.S. Different processing phases for features, figures and selective attention in the primary visual cortex. Neuron 56, 785–792 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Poort, J. et al. The role of attention in figure-ground segregation in areas V1 and V4 of the visual cortex. Neuron 75, 143–156 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. Roelfsema, P.R. & Spekreijse, H. The representation of erroneously perceived stimuli in the primary visual cortex. Neuron [see comments] 31, 853–863 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. Supèr, H., Spekreijse, H. & Lamme, V.A. Two distinct modes of sensory processing observed in monkey primary visual cortex (V1). Nat. Neurosci. 4, 304–310 (2001).

    Article  PubMed  Google Scholar 

  39. Lee, T.S., Yang, C.F., Romero, R.D. & Mumford, D. Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency. Nat. Neurosci. 5, 589–597 (2002).

    Article  CAS  PubMed  Google Scholar 

  40. Piëch, V., Li, W., Reeke, G.N. & Gilbert, C.D. Network model of top-down influences on local gain and contextual interactions in visual cortex. Proc. Natl. Acad. Sci. USA 110, E4108–E4117 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Byers, A. & Serences, J.T. Exploring the relationship between perceptual learning and top-down attentional control. Vision Res. 74, 30–39 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Gilbert, C.D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Shiu, L.P. & Pashler, H. Improvement in line orientation discrimination is retinally local but dependent on cognitive set. Percept. Psychophys. 52, 582–588 (1992).

    Article  CAS  PubMed  Google Scholar 

  44. Ahissar, M. & Hochstein, S. Attentional control of early perceptual learning. Proc. Natl. Acad. Sci. USA 90, 5718–5722 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Poggio, T., Fahle, M. & Edelman, S. Fast perceptual learning in visual hyperacuity. Science 256, 1018–1021 (1992).

    Article  CAS  PubMed  Google Scholar 

  46. Karni, A. & Sagi, D. The time course of learning a visual skill. Nature 365, 250–252 (1993).

    Article  CAS  PubMed  Google Scholar 

  47. Dosher, B.A. & Lu, Z.L. Mechanisms of perceptual learning. Vision Res. 39, 3197–3221 (1999).

    Article  CAS  PubMed  Google Scholar 

  48. Bejjanki, V.R., Beck, J.M., Lu, Z.L. & Pouget, A. Perceptual learning as improved probabilistic inference in early sensory areas. Nat. Neurosci. 14, 642–648 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dosher, B.A. & Lu, Z.L. Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proc. Natl. Acad. Sci. USA 95, 13988–13993 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gold, J., Bennett, P.J. & Sekuler, A.B. Signal but not noise changes with perceptual learning. Nature 402, 176–178 (1999).

    Article  CAS  PubMed  Google Scholar 

  51. Shoham, S., Fellows, M.R. & Normann, R.A. Robust, automatic spike sorting using mixtures of multivariate t-distributions. J. Neurosci. Methods 127, 111–122 (2003).

    Article  PubMed  Google Scholar 

  52. Gretton, A., Borgwardt, K.M., Rasch, M.J., Scholkopf, B. & Smola, A. A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012).

    Google Scholar 

  53. Rasch, M.J., Gretton, A., Murayama, Y., Maass, W. & Logothetis, N.K. Inferring spike trains from local field potentials. J. Neurophysiol. 99, 1461–1476 (2008).

    Article  PubMed  Google Scholar 

  54. Fisher, R.A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936).

    Article  Google Scholar 

Download references

Acknowledgements

We thank X. Xu and F. Wang for technical assistance. This work was supported by the National Basic Research Program of China (973 Program 2014CB846101, 2011CBA00400), the National Natural Science Foundation of China (31125014, 31371109 and 30970983) and the Fundamental Research Funds for the Central Universities of China.

Author information

Authors and Affiliations

Authors

Contributions

M.C., Y.Y. and W.L. designed the experiments. Y.Y., M.C. and X.X. conducted the experiments. M.J.R., Y.Y., M.H. and S.W. analyzed the data. M.J.R. performed the population decoding analyses. M.J.R. and Y.Y. prepared the figures. M.J.R., Y.Y. and W.L. wrote the paper.

Corresponding author

Correspondence to Wu Li.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yan, Y., Rasch, M., Chen, M. et al. Perceptual training continuously refines neuronal population codes in primary visual cortex. Nat Neurosci 17, 1380–1387 (2014). https://doi.org/10.1038/nn.3805

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3805

Further reading

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