Contextual modulation of V1 receptive fields depends on their spatial symmetry



The apparent receptive field characteristics of sensory neurons depend on the statistics of the stimulus ensemble—a nonlinear phenomenon often called contextual modulation. Since visual cortical receptive fields determined from simple stimuli typically do not predict responses to complex stimuli, understanding contextual modulation is crucial to understanding responses to natural scenes. To analyze contextual modulation, we examined how apparent receptive fields differ for two stimulus ensembles that are matched in first- and second-order statistics, but differ in their feature content: one ensemble is enriched in elongated contours. To identify systematic trends across the neural population, we used a multidimensional scaling method, the Procrustes transformation. We found that contextual modulation of receptive field components increases with their spatial extent. More surprisingly, we also found that odd-symmetric components change systematically, but even-symmetric components do not. This symmetry dependence suggests that contextual modulation is driven by oriented On/Off dyads, i.e., modulation of the strength of intracortically-generated signals.


Primary visual cortex Plasticity Linear–nonlinear model Reverse correlation 

Supplementary material

10827_2008_107_MOESM1_ESM.pdf (330 kb)
ESM 1(PDF 338 KB)


  1. Baccus, S. A., & Meister, M. (2002). Fast and slow contrast adaptation in retinal circuitry. Neuron, 36, 909–919. doi:10.1016/S0896-6273(02)01050-4.PubMedCrossRefGoogle Scholar
  2. Bonds, A. B. (1989). Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex. Visual Neuroscience, 2, 41–55.PubMedGoogle Scholar
  3. Carandini, M., Heeger, D. J., & Movshon, J. A. (1997). Linearity and normalization in simple cells of the macaque primary visual cortex. The Journal of Neuroscience, 17, 8621–8644.PubMedGoogle Scholar
  4. Carandini, M., Heeger, D. J., & Senn, W. (2002). A synaptic explanation of suppression in visual cortex. The Journal of Neuroscience, 22, 10053–10065.PubMedGoogle Scholar
  5. Chander, D., & Chichilnisky, E. J. (2001). Adaptation to temporal contrast in primate and salamander retina. The Journal of Neuroscience, 21, 9904–9916.PubMedGoogle Scholar
  6. Chapman, B., & Godecke, I. (2000). Cortical cell orientation selectivity fails to develop in the absence of ON-center retinal ganglion cell activity. The Journal of Neuroscience, 20, 1922–1930.PubMedGoogle Scholar
  7. Cox, T. F., & Cox, M. A. A.(2000). Multidimensional scaling, Second edn: Chapman and Hall).Google Scholar
  8. Das, A., & Gilbert, C. D. (1999). Topography of contextual modulations mediated by short-range interactions in primary visual cortex. Nature, 399, 655–661. doi:10.1038/21371.PubMedCrossRefGoogle Scholar
  9. David, S. V., Vinje, W. E., & Gallant, J. L. (2004). Natural stimulus statistics alter the receptive field structure of V1 neurons. The Journal of Neuroscience, 24, 6991–7006. doi:10.1523/JNEUROSCI.1422-04.2004.PubMedCrossRefGoogle Scholar
  10. de Boer, E., & Kuyper, P. (1968). Triggered correlation. IEEE Transactions on Bio-Medical Engineering, 15, 169–179. doi:10.1109/TBME.1968.4502561.PubMedCrossRefGoogle Scholar
  11. De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982a). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research, 22, 545–559. doi:10.1016/0042-6989(82)90113-4.PubMedCrossRefGoogle Scholar
  12. De Valois, R. L., & Thorell, L. G. (1988). Spatial vision. New York: Oxford University Press.Google Scholar
  13. De Valois, R. L., Yund, E. W., & Helper, N. (1982b). The orientation and direction selectivity of cells in macaque visual cortex. Vision Research, 22, 531–544. doi:10.1016/0042-6989(82)90112-2.PubMedCrossRefGoogle Scholar
  14. Efron, B., & Tibshirani, R. J. (1998). An introduction to the bootstrap. London: Chapman and Hall/CRC.Google Scholar
  15. Felsen, G., Touryan, J., Han, F., & Dan, Y. (2005). Cortical sensitivity to visual features in natural scenes. PLoS Biology, 3, e342. doi:10.1371/journal.pbio.0030342.PubMedCrossRefGoogle Scholar
  16. Freeman, T. C., Durand, S., Kiper, D. C., & Carandini, M. (2002). Suppression without inhibition in visual cortex. Neuron, 35, 759–771. doi:10.1016/S0896-6273(02)00819-X.PubMedCrossRefGoogle Scholar
  17. Heeger, D. J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 181–198.PubMedGoogle Scholar
  18. Hosoya, T., Baccus, S. A., & Meister, M. (2005). Dynamic predictive coding by the retina. Nature, 436, 71–77. doi:10.1038/nature03689.PubMedCrossRefGoogle Scholar
  19. Hsu, A., Woolley, S. M., Fremouw, T. E., & Theunissen, F. E. (2004). Modulation power and phase spectrum of natural sounds enhance neural encoding performed by single auditory neurons. The Journal of Neuroscience, 24, 9201–9211. doi:10.1523/JNEUROSCI.2449-04.2004.PubMedCrossRefGoogle Scholar
  20. Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurons in the cat's striate cortex. The Journal of Physiology, 148, 574–591.PubMedGoogle Scholar
  21. Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology, 195, 215–243.PubMedGoogle Scholar
  22. Kabsch, W. (1976). A solution for the best rotation to relate two sets of vectors. Acta Crystallographica. Section A, 32, 922–923. doi:10.1107/S0567739476001873.CrossRefGoogle Scholar
  23. Kim, K. J., & Rieke, F. (2001). Temporal contrast adaptation in the input and output signals of salamander retinal ganglion cells. The Journal of Neuroscience, 21, 287–299.PubMedGoogle Scholar
  24. Meister, M., & Berry, M. J. (1999). The neural code of the retina. Neuron, 22, 435–450. doi:10.1016/S0896-6273(00)80700-X.PubMedCrossRefGoogle Scholar
  25. Morrone, M. C., & Burr, D. C. (1988). Feature detection in human vision: a phase-dependent energy model. Proceedings of the Royal Society of London—Series B: Biological Sciences, 235, 221–245.Google Scholar
  26. Ohzawa, I., Sclar, G., & Freeman, R. D. (1985). Contrast gain control in the cat's visual system. Journal of Neurophysiology, 54, 651–667.PubMedGoogle Scholar
  27. Oppenheim, A. V., & Lim, J. S. (1981). The importance of phase in signals. Proceedings of the IEEE, 69, 529–541.CrossRefGoogle Scholar
  28. Priebe, N. J., & Ferster, D. (2006). Mechanisms underlying cross-orientation suppression in cat visual cortex. Nature Neuroscience, 9, 552–561. doi:10.1038/nn1660.PubMedCrossRefGoogle Scholar
  29. Reich, D. S. (2001) PhD Thesis: Information encoding by individual neurons and groups of neurons in primary visual cortex, The Rockefeller University, New York.Google Scholar
  30. Rieke, F., Bodnar, D. A., & Bialek, W. (1995). Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents. Proceedings of the Royal Society of London—Series B: Biological Sciences, 262, 259–265. doi:10.1098/rspb.1995.0204.PubMedCrossRefGoogle Scholar
  31. Ringach, D. L. (2004). Haphazard wiring of simple receptive fields and orientation columns in visual cortex. Journal of Neurophysiology, 92, 468–476. doi:10.1152/jn.01202.2003.PubMedCrossRefGoogle Scholar
  32. Ringach, D. L. (2007). On the origin of the functional architecture of the cortex. PLoS ONE, 2, e251. doi:10.1371/journal.pone.0000251.
  33. Ruderman, D. L., & Bialek, W. (1994). Statistics of natural images: Scaling in the woods. Physical Review Letters, 73, 814–817. doi:10.1103/PhysRevLett.73.814.PubMedCrossRefGoogle Scholar
  34. Schwartz, O., Pillow, J. W., Rust, N. C., & Simoncelli, E. P. (2006). Spike-triggered neural characterization. Journal of Vision (Charlottesville, Va.), 6, 484–507. doi:10.1167/6.4.13.Google Scholar
  35. Shapley, R. M., & Victor, J. D. (1978). The effect of contrast on the transfer properties of cat retinal ganglion cells. The Journal of Physiology, 285, 275–298.PubMedGoogle Scholar
  36. Sharpee, T., Rust, N. C., & Bialek, W. (2004). Analyzing neural responses to natural signals: maximally informative dimensions. Neural Computation, 16, 223–250. doi:10.1162/089976604322742010.PubMedCrossRefGoogle Scholar
  37. Sharpee, T. O., Sugihara, H., Kurgansky, A. V., Rebrik, S. P., Stryker, M. P., & Miller, K. D. (2006). Adaptive filtering enhances information transmission in visual cortex. Nature, 439, 936–942. doi:10.1038/nature04519.PubMedCrossRefGoogle Scholar
  38. Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience, 24, 1193–1216. doi:10.1146/annurev.neuro.24.1.1193.PubMedCrossRefGoogle Scholar
  39. Smirnakis, S. M., Berry, M. J., Warland, D. K., Bialek, W., & Meister, M. (1997). Adaptation of retinal processing to image contrast and spatial scale. Nature, 386, 69–73. doi:10.1038/386069a0.PubMedCrossRefGoogle Scholar
  40. Soodak, R. E. (1987). The retinal ganglion cell mosaic defines orientation columns in striate cortex. Proceedings of the National Academy of Sciences of the United States of America, 84, 3936–3940. doi:10.1073/pnas.84.11.3936.PubMedCrossRefGoogle Scholar
  41. Theunissen, F. E., David, S. V., Singh, N. C., Hsu, A., Vinje, W. E., & Gallant, J. L. (2001). Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network, 12, 289–316.PubMedGoogle Scholar
  42. Theunissen, F. E., Sen, K., & Doupe, A. J. (2000). Spectral–temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. The Journal of Neuroscience, 20, 2315–2331.PubMedGoogle Scholar
  43. Victor, J., & Shapley, R. (1980). A method of nonlinear analysis in the frequency domain. Biophysical Journal, 29, 459–483.PubMedCrossRefGoogle Scholar
  44. Victor, J. D., Mechler, F., Repucci, M. A., Purpura, K. P., & Sharpee, T. (2006). Responses of V1 neurons to two-dimensional hermite functions. Journal of Neurophysiology, 95, 379–400. doi:10.1152/jn.00498.2005.PubMedCrossRefGoogle Scholar
  45. Vinje, W. E., & Gallant, J. L. (2000). Sparse coding and decorrelation in primary visual cortex during natural vision. Science, 287, 1273–1276. doi:10.1126/science.287.5456.1273.PubMedCrossRefGoogle Scholar
  46. Vinje, W. E., & Gallant, J. L. (2002). Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1. The Journal of Neuroscience, 22, 2904–2915.PubMedGoogle Scholar
  47. Wassle, H., Boycott, B. B., & Illing, R. B. (1981). Morphology and mosaic of on- and off-beta cells in the cat retina and some functional considerations. Proceedings of the Royal Society of London—Series B: Biological Sciences, 212, 177–195.CrossRefGoogle Scholar
  48. Woolley, S. M., Gill, P. R., & Theunissen, F. E. (2006). Stimulus-dependent auditory tuning results in synchronous population coding of vocalizations in the songbird midbrain. The Journal of Neuroscience, 26, 2499–2512. doi:10.1523/JNEUROSCI.3731-05.2006.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Computational Neurobiology LaboratoryThe Salk Institute for Biological StudiesLa JollaUSA
  2. 2.Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA
  3. 3.Weill Cornell Medical CollegeNew YorkUSA

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