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Perceptual learning as improved probabilistic inference in early sensory areas

Nature Neuroscience volume 14, pages 642–648 (2011)Cite this article

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Abstract

Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations.

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Figure 1: Neural and behavioral correlates of perceptual learning.
Figure 2: Network architecture.
Figure 3: Modeling perceptual learning using a realistic neural model of orientation discrimination.
Figure 4: Perceptual learning and tuning curves: the role of amplification and sharpening.
Figure 5: The effect of perceptual learning on noise correlations.
Figure 6: TVC curves computed from responses in which noise correlations have been removed through shuffling.
Figure 7: The effect of subsampling.

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References

  1. Ramachandran, V.S. & Braddick, O. Orientation-specific learning in stereopsis. Perception 2, 371–376 (1973).

    Article  CAS  Google Scholar 

  2. Fahle, M., Edelman, S. & Poggio, T. Fast perceptual learning in hyperacuity. Vision Res. 35, 3003–3013 (1995).

    Article  CAS  Google Scholar 

  3. Fiorentini, A. & Berardi, N. Perceptual learning specific for orientation and spatial frequency. Nature 287, 43–44 (1980).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Fine, I. & Jacobs, R.A. Comparing perceptual learning tasks: A review. J. Vis. 2, 190–203 (2002).

    PubMed  Google Scholar 

  6. Recanzone, G.H., Merzenich, M.M., Jenkins, W.M., Grajski, K.A. & Dinse, H.R. Topographic reorganization of the hand representation in cortical area 3b owl monkeys trained in a frequency-discrimination task. J. Neurophysiol. 67, 1031–1056 (1992).

    Article  CAS  Google Scholar 

  7. Recanzone, G.H., Schreiner, C.E. & Merzenich, M.M. Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J. Neurosci. 13, 87–103 (1993).

    Article  CAS  Google Scholar 

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

    Article  CAS  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  Google Scholar 

  10. Ahissar, M. & Hochstein, S. Task difficulty and the specificity of perceptual learning. Nature 387, 401–406 (1997).

    Article  CAS  Google Scholar 

  11. Fahle, M. & Morgan, M. No transfer of perceptual learning between similar stimuli in the same retinal position. Curr. Biol. 6, 292–297 (1996).

    Article  CAS  Google Scholar 

  12. Karni, A. & Sagi, D. Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proc. Natl. Acad. Sci. USA 88, 4966–4970 (1991).

    Article  CAS  Google Scholar 

  13. Xiao, L.Q. et al. Complete transfer of perceptual learning across retinal locations enabled by double training. Curr. Biol. 18, 1922–1926 (2008).

    Article  CAS  Google Scholar 

  14. Zhang, J.-Y. et al. Rule-based learning explains visual perceptual learning and its specificity and transfer. J. Neurosci. 30, 12323–12328 (2010).

    Article  CAS  Google Scholar 

  15. Watanabe, T. et al. Greater plasticity in lower-level than higher-level visual motion processing in a passive perceptual learning task. Nat. Neurosci. 5, 1003–1009 (2002).

    Article  CAS  Google Scholar 

  16. 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  Google Scholar 

  17. Schwabe, L. & Obermayer, K. Adaptivity of tuning functions in a generic recurrent network model of a cortical hypercolumn. J. Neurosci. 25, 3323–3332 (2005).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  19. 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 

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

    Article  CAS  Google Scholar 

  21. 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 

  22. 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 

  23. 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  Google Scholar 

  24. Levi, D.M. & Klein, S.A. Noise provides some new signals about the spatial vision of amblyopes. J. Neurosci. 23, 2522–2526 (2003).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. Li, R.W., Levi, D.M. & Klein, S.A. Perceptual learning improves efficiency by re-tuning the decision 'template' for position discrimination. Nat. Neurosci. 7, 178–183 (2004).

    Article  CAS  Google Scholar 

  27. 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  Google Scholar 

  28. 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  Google Scholar 

  29. Lu, Z.-L., Liu, J. & Dosher, B.A. Modeling mechanisms of perceptual learning with augmented Hebbian re-weighting. Vision Res. 50, 375–390 (2010).

    Article  Google Scholar 

  30. 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 

  31. Law, C.-T. & Gold, J.I. Reinforcement learning can account for associative and perceptual learning on a visual-decision task. Nat. Neurosci. 12, 655–663 (2009).

    Article  CAS  Google Scholar 

  32. 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 

  33. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  35. Beck, J.M., Bejjanki, V.R. & Pouget, A. Insights from a simple expression for linear Fisher information in a recurrently connected population of spiking neurons. Neural Comput. (in the press).

  36. Ghose, G.M., Yang, T. & Maunsell, J.H.R. Physiological correlates of perceptual learning in monkey V1 and V2. J. Neurophysiol. 87, 1867–1888 (2002).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  38. 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 

  39. 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 

  40. Jacobs, R.A. Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning. J. Vis. 9, 22 (2009).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  42. Gold, J.I. & Shadlen, M.N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 5, 10–16 (2001).

    Article  Google Scholar 

  43. 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 

  44. Simoncelli, E.P., Adelson, E.H. & Heeger, D.J. Probability distributions of optical flow. in IEEE Conference on Computer Vision and Pattern Recognition 310–315 (1991).

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

    Article  CAS  Google Scholar 

  46. Pouget, A., Deneve, S. & Latham, P.E. The relevance of Fisher information for theories of cortical computation and attention. in Visual Attention and Cortical Circuits (eds. Braun, J., Koch, C. & Davis, J.L.) (2001).

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

    Article  CAS  Google Scholar 

  48. Treue, S. & Maunsell, J.H.R. Attentional modulation of visual motion processing in cortical areas MT and MST. Nature 382, 539–541 (1996).

    Article  CAS  Google Scholar 

  49. Gerstner, W. & Kistler, W. Spiking Neuron Models: An Introduction (Cambridge Univ. Press, New York, 2002).

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

    Article  CAS  Google Scholar 

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Acknowledgements

J.M.B. was supported by the Gatsby Charitable Foundation. Z.-L.L. is supported by US National Eye Institute grant 9 R01 EY017491-05 and A.P. by Multidisciplinary University Research Initiative grant N00014-07-1-0937, US National Institute on Drug Abuse grant BCS0346785 and a research grant from the James S. McDonnell Foundation. This work was also partially supported by award P30 EY001319 from the US National Eye Institute.

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Author notes

  1. Vikranth R Bejjanki and Jeffrey M Beck: These authors contributed equally to this work.

Authors and Affiliations

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

    Vikranth R Bejjanki, Jeffrey M Beck & Alexandre Pouget

  2. Gatsby Computational Neuroscience Unit, London, UK

    Jeffrey M Beck

  3. Department of Psychology, University of Southern California, Los Angeles, California, USA

    Zhong-Lin Lu

Authors
  1. Vikranth R Bejjanki
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  2. Jeffrey M Beck
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Contributions

V.R.B. conceived the project, built the network model, ran all the simulations and analyses and wrote the paper. J.M.B. developed the analytic derivations, helped with building the network model and wrote the paper. Z.-L.L. worked on the link between the neural model and TVC curves and helped with parameter tuning. A.P. conceived the project, supervised the simulations and analyses and wrote the paper.

Corresponding author

Correspondence to Alexandre Pouget.

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The authors declare no competing financial interests.

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Bejjanki, V., Beck, J., Lu, ZL. et al. Perceptual learning as improved probabilistic inference in early sensory areas. Nat Neurosci 14, 642–648 (2011). https://doi.org/10.1038/nn.2796

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