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Adaptation of the simple or complex nature of V1 receptive fields to visual statistics

Abstract

Receptive fields in primary visual cortex (V1) are categorized as simple or complex, depending on their spatial selectivity to stimulus contrast polarity. We studied the dependence of this classification on visual context by comparing, in the same cell, the synaptic responses to three classical receptive field mapping protocols: sparse noise, ternary dense noise and flashed Gabor noise. Intracellular recordings revealed that the relative weights of simple-like and complex-like receptive field components were scaled so as to make the same receptive field more simple-like with dense noise stimulation and more complex-like with sparse or Gabor noise stimulations. However, once these context-dependent receptive fields were convolved with the corresponding stimulus, the balance between simple-like and complex-like contributions to the synaptic responses appeared to be invariant across input statistics. This normalization of the linear/nonlinear input ratio suggests a previously unknown form of homeostatic control of V1 functional properties, optimizing the network nonlinearities to the statistical structure of the visual input.

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Figure 1: White-noise stimuli and second-order Volterra receptive field decomposition.
Figure 2: Stimulus dependence of simple-like and complex-like receptive field components.
Figure 3: Receptive field Simpleness and gain control of simple-like and complex-like receptive field components.
Figure 4: Spatiotemporal reconfiguration of simple-like and complex-like receptive field components.
Figure 5: V1 receptive field simpleness adapts to visual statistics.
Figure 6: Simpleness in non-adaptive receptive field models.
Figure 7: Simpleness in gain control receptive field models.

References

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

    CAS  Article  Google Scholar 

  2. Movshon, J.A., Thompson, I. & Tolhurst, D. Spatial summation in the receptive fields of simple cells in the cat's striate cortex. J. Physiol. (Lond.) 283, 53–77 (1978).

    CAS  Article  Google Scholar 

  3. DeAngelis, G.C., Ohzawa, I. & Freeman, R.D. Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. J. Neurophysiol. 69, 1118–1135 (1993).

    CAS  Article  Google Scholar 

  4. Carandini, M. et al. Do we know what the early visual system does? J. Neurosci. 25, 10577–10597 (2005).

    CAS  Article  Google Scholar 

  5. Movshon, J.A., Thompson, I. & Tolhurst, D. Receptive field organization of complex cells in the cat's striate cortex. J. Physiol. (Lond.) 283, 79–99 (1978).

    CAS  Article  Google Scholar 

  6. Dean, A.F. & Tolhurst, D.J. On the distinctness of simple and complex cells in the visual cortex of the cat. J. Physiol. (Lond.) 344, 305–325 (1983).

    CAS  Article  Google Scholar 

  7. Emerson, R.C. et al. Nonlinear directionally selective subunits in complex cells of cat striate cortex. J. Neurophysiol. 58, 33–65 (1987).

    CAS  Article  Google Scholar 

  8. Skottun, B.C. et al. Classifying simple and complex cells on the basis of response modulation. Vision Res. 31, 1079–1086 (1991).

    CAS  Google Scholar 

  9. Priebe, N.J. et al. The contribution of spike threshold to the dichotomy of cortical simple and complex cells. Nat. Neurosci. 7, 1113–1122 (2004).

    CAS  Article  Google Scholar 

  10. Chance, F.S., Nelson, S.B. & Abbott, L.F. Complex cells as cortically amplified simple cells. Nat. Neurosci. 2, 277–282 (1999).

    CAS  Article  Google Scholar 

  11. Tao, L. et al. An egalitarian network model for the emergence of simple and complex cells in visual cortex. Proc. Natl. Acad. Sci. USA 101, 366–371 (2004).

    CAS  Article  Google Scholar 

  12. Rust, N.C. et al. Spatiotemporal elements of macaque v1 receptive fields. Neuron 46, 945–956 (2005).

    CAS  Article  Google Scholar 

  13. Bardy, C. et al. “Simplification” of responses of complex cells in cat striate cortex: suppressive surrounds and “feedback” inactivation. J. Physiol. (Lond.) 574, 731–750 (2006).

    CAS  Article  Google Scholar 

  14. Victor, J.D. et al. Responses of V1 neurons to two-dimensional hermite functions. J. Neurophysiol. 95, 379–400 (2006).

    Article  Google Scholar 

  15. Albrecht, D.G., Farrar, S.B. & Hamilton, D.B. Spatial contrast adaptation characteristics of neurones recorded in the cat's visual cortex. J. Physiol. (Lond.) 347, 713–739 (1984).

    CAS  Article  Google Scholar 

  16. Ohzawa, I., Sclar, G. & Freeman, R.D. Contrast gain control in the cat's visual system. J. Neurophysiol. 54, 651–667 (1985).

    CAS  Article  Google Scholar 

  17. Carandini, M. & Ferster, D. A tonic hyperpolarization underlying contrast adaptation in cat visual cortex. Science 276, 949–952 (1997).

    CAS  Article  Google Scholar 

  18. Sanchez-Vives, M.V., Nowak, L.G. & McCormick, D.A. Membrane mechanisms underlying contrast adaptation in cat area 17 in vivo. J. Neurosci. 20, 4267–4285 (2000).

    CAS  Article  Google Scholar 

  19. Sharpee, T.O. et al. Adaptive filtering enhances information transmission in visual cortex. Nature 439, 936–942 (2006).

    CAS  Article  Google Scholar 

  20. Felsen, G. et al. Cortical sensitivity to visual features in natural scenes. PLoS Biol. 3, e342 (2005).

    Article  Google Scholar 

  21. David, S.V., Vinje, W.E. & Gallant, J.L. Natural stimulus statistics alter the receptive field structure of v1 neurons. J. Neurosci. 24, 6991–7006 (2004).

    CAS  Article  Google Scholar 

  22. Yeh, C.I. et al. Stimulus ensemble and cortical layer determine V1 spatial receptive fields. Proc. Natl. Acad. Sci. USA 106, 14652–14657 (2009).

    CAS  Article  Google Scholar 

  23. Victor, J.D. et al. Laminar and orientation-dependent characteristics of spatial nonlinearities: implications for the computational architecture of visual cortex. J. Neurophysiol. 102, 3414–3432 (2009).

    Article  Google Scholar 

  24. Mata, M.L. & Ringach, D.L. Spatial overlap of ON and OFF subregions and its relation to response modulation ratio in macaque primary visual. J. Neurophysiol. 93, 919–928 (2005).

    Article  Google Scholar 

  25. Stryker, M. & Blakemore, C. Saccadic and disjunctive eye movements in cats. Vision Res. 12, 2005–2013 (1972).

    CAS  Article  Google Scholar 

  26. Carandini, M. & Heeger, D.J. Summation and division by neurons in primate visual cortex. Science 264, 1333–1336 (1994).

    CAS  Article  Google Scholar 

  27. Crowder, N.A. et al. Complex cells increase their phase sensitivity at low contrasts and following adaptation. J. Neurophysiol. 98, 1155–1166 (2007).

    CAS  Article  Google Scholar 

  28. Aertsen, A.M. et al. Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61, 900–917 (1989).

    CAS  Article  Google Scholar 

  29. Martinez, L.M. & Alonso, J.M. Construction of complex receptive fields in cat primary visual cortex. Neuron 32, 515–525 (2001).

    CAS  Article  Google Scholar 

  30. Martinez, L.M. et al. Receptive field structure varies with layer in the primary visual cortex. Nat. Neurosci. 8, 372–379 (2005).

    CAS  Article  Google Scholar 

  31. LeVay, S. & Gilbert, C.D. Laminar patterns of geniculocortical projection in the cat. Brain Res. 113, 1–19 (1976).

    CAS  Article  Google Scholar 

  32. Lesica, N.A. et al. Adaptation to stimulus contrast and correlations during natural visual stimulation. Neuron 55, 479–491 (2007).

    CAS  Article  Google Scholar 

  33. Smirnakis, S.M. et al. Adaptation of retinal processing to image contrast and spatial scale. Nature 386, 69–73 (1997).

    CAS  Article  Google Scholar 

  34. So, Y.T. & Shapley, R. Spatial tuning of cells in and around lateral geniculate nucleus of the cat: X and Y relay cells and perigeniculate interneurons. J. Neurophysiol. 45, 107–120 (1981).

    CAS  Article  Google Scholar 

  35. Sillito, A.M. The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat. J. Physiol. (Lond.) 250, 305–329 (1975).

    CAS  Article  Google Scholar 

  36. Troyer, T.W. et al. Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J. Neurosci. 18, 5908–5927 (1998).

    CAS  Article  Google Scholar 

  37. Lauritzen, T.Z. & Miller, K.D. Different roles for simple-cell and complex-cell inhibition in V1. J. Neurosci. 23, 10201–10213 (2003).

    CAS  Article  Google Scholar 

  38. Hirsch, J.A. et al. Functionally distinct inhibitory neurons at the first stage of visual cortical processing. Nat. Neurosci. 6, 1300–1308 (2003).

    CAS  Article  Google Scholar 

  39. Liu, B.H. et al. Visual receptive field structure of cortical inhibitory neurons revealed by two-photon imaging guided recording. J. Neurosci. 29, 10520–10532 (2009).

    CAS  Article  Google Scholar 

  40. Debanne, D., Shulz, D.E. & Fregnac, Y. Activity-dependent regulation of “on” and “off” responses in cat visual cortical receptive fields. J. Physiol. (Lond.) 508, 523–548 (1998).

    CAS  Article  Google Scholar 

  41. Nauhaus, I. et al. Stimulus contrast modulates functional connectivity in visual cortex. Nat. Neurosci. 12, 70–76 (2009).

    CAS  Article  Google Scholar 

  42. Müller, J.R. et al. Rapid adaptation in visual cortex to the structure of images. Science 285, 1405–1408 (1999).

    Article  Google Scholar 

  43. Felsen, G. et al. Dynamic modification of cortical orientation tuning mediated by recurrent connections. Neuron 36, 945–954 (2002).

    CAS  Article  Google Scholar 

  44. Nagel, K.I. & Doupe, A.J. Temporal processing and adaptation in the songbird auditory forebrain. Neuron 51, 845–859 (2006).

    CAS  Article  Google Scholar 

  45. Maravall, M. et al. Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex. PLoS Biol. 5, e19 (2007).

    Article  Google Scholar 

  46. Higley, M.J. & Contreras, D. Frequency adaptation modulates spatial integration of sensory responses in the rat whisker system. J. Neurophysiol. 97, 3819–3824 (2007).

    Article  Google Scholar 

  47. Brenner, N., Bialek, W. & de Ruyter van Steveninck, R. Adaptive rescaling maximizes information transmission. Neuron 26, 695–702 (2000).

    CAS  Article  Google Scholar 

  48. Wainwright, M.J. Visual adaptation as optimal information transmission. Vision Res. 39, 3960–3974 (1999).

    CAS  Article  Google Scholar 

  49. Westwick, D.T. & Kearney, R.E. Identification of Nonlinear Physiological Systems (Wiley-IEEE, 2003).

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Acknowledgements

We are thankful to G. Sadoc for his invaluable technical assistance in developing the stimulation software and kernel analysis library tools. We thank A. Davison for comments and suggestions on the manuscript. We are thankful to Z. Kisvarday and K. Sari for their help in the biocytin labeling protocol. We acknowledge the financial support of CNRS, the Agence Nationale de la Recherche (Natstats and V1-Complex), European community contracts Facets (FP6-2004-IST-FETPI 15879) and Brain-i-nets (FP7-2009-ICT-FET 243914).

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Contributions

The study was conceived by J.F., C.M. and Y.F. The experiments were performed by J.F., C.M. and M.P. J.F. performed the data analysis and model simulations. J.F., C.M. and Y.F. wrote the paper.

Corresponding authors

Correspondence to Julien Fournier or Yves Frégnac.

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The authors declare no competing financial interests.

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Fournier, J., Monier, C., Pananceau, M. et al. Adaptation of the simple or complex nature of V1 receptive fields to visual statistics. Nat Neurosci 14, 1053–1060 (2011). https://doi.org/10.1038/nn.2861

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