A natural approach to studying vision


An ultimate goal of systems neuroscience is to understand how sensory stimuli encountered in the natural environment are processed by neural circuits. Achieving this goal requires knowledge of both the characteristics of natural stimuli and the response properties of sensory neurons under natural stimulation. Most of our current notions of sensory processing have come from experiments using simple, parametric stimulus sets. However, a growing number of researchers have begun to question whether this approach alone is sufficient for understanding the real-life sensory tasks performed by the organism. Here, focusing on the early visual pathway, we argue that the use of natural stimuli is vital for advancing our understanding of sensory processing.

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  1. 1

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

    CAS  Article  Google Scholar 

  2. 2

    Hartline, H.K. The receptive fields of optic nerve fibers. Am. J. Physiol. 130, 690–699 (1940).

    Article  Google Scholar 

  3. 3

    Hubel, D.H. & Wiesel, T.N. Integrative action in the cat's lateral geniculate body. J. Physiol. (Lond.) 155, 385–398 (1961).

    CAS  Article  Google Scholar 

  4. 4

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

    CAS  Article  Google Scholar 

  5. 5

    Reid, R.C., Victor, J.D. & Shapley, R.M. The use of m-sequences in the analysis of visual neurons: linear receptive field properties. Vis. Neurosci. 14, 1015–1027 (1997).

    CAS  Article  Google Scholar 

  6. 6

    Marmarelis, P.Z. & Marmarelis, V.Z. Analysis of Physiological Systems: The White-Noise Approach (Plenum, New York, 1978).

    Google Scholar 

  7. 7

    Ringach, D.L., Hawken, M.J. & Shapley, R. Dynamics of orientation tuning in macaque primary visual cortex. Nature 387, 281–284 (1997).

    CAS  Article  Google Scholar 

  8. 8

    Mazer, J.A., Vinje, W.E., McDermott, J., Schiller, P.H. & Gallant, J.L. Spatial frequency and orientation tuning dynamics in area V1. Proc. Natl. Acad. Sci. USA 99, 1645–1650 (2002).

    CAS  Article  Google Scholar 

  9. 9

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

    CAS  Article  Google Scholar 

  10. 10

    Bredfeldt, C.E. & Ringach, D.L. Dynamics of spatial frequency tuning in macaque V1. J. Neurosci. 22, 1976–1984 (2002).

    CAS  Article  Google Scholar 

  11. 11

    Touryan, J., Felsen, G. & Dan, Y. Spatial structure of complex cell receptive fields measured with natural images. Neuron 45, 781–791 (2005).

    CAS  Article  Google Scholar 

  12. 12

    Felsen, G., Touryan, J., Han, F. & Dan, Y. Cortical sensitivity to visual features in natural scenes. PLoS Biol. 3, 1819–1828 (2005).

    CAS  Article  Google Scholar 

  13. 13

    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 

  14. 14

    Ringach, D.L., Hawken, M.J. & Shapley, R. Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. J. Vis. 2, 12–24 (2002).

    Article  Google Scholar 

  15. 15

    Smyth, D., Willmore, B., Baker, G.E., Thompson, I.D. & Tolhurst, D.J. The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation. J. Neurosci. 23, 4746–4759 (2003).

    CAS  Article  Google Scholar 

  16. 16

    Sharpee, T., Rust, N.C. & Bialek, W. Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput. 16, 223–250 (2004).

    Article  Google Scholar 

  17. 17

    Prenger, R., Wu, M.C., David, S.V. & Gallant, J.L. Nonlinear V1 responses to natural scenes revealed by neural network analysis. Neural Netw. 17, 663–679 (2004).

    Article  Google Scholar 

  18. 18

    Dan, Y., Atick, J.J. & Reid, R.C. Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. J. Neurosci. 16, 3351–3362 (1996).

    CAS  Article  Google Scholar 

  19. 19

    Adelson, E.H. & Bergen, J.R. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).

    CAS  Article  Google Scholar 

  20. 20

    Heeger, D.J. Normalization of cell responses in cat striate cortex. Vis. Neurosci. 9, 181–197 (1992).

    CAS  Article  Google Scholar 

  21. 21

    Mechler, F., Reich, D.S. & Victor, J.D. Detection and discrimination of relative spatial phase by V1 neurons. J. Neurosci. 22, 6129–6157 (2002).

    CAS  Article  Google Scholar 

  22. 22

    Rieke, F., Bodnar, D.A. & Bialek, W. Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents. Proc. Biol. Sci. 262, 259–265 (1995).

    CAS  Article  Google Scholar 

  23. 23

    Woolley, S.M., Fremouw, T.E., Hsu, A. & Theunissen, F.E. Tuning for spectro-temporal modulations as a mechanism for auditory discrimination of natural sounds. Nat. Neurosci. 8, 1371–1379 (2005).

    CAS  Article  Google Scholar 

  24. 24

    Attneave, F. Some informational aspects of visual perception. Psychol. Rev. 61, 183–193 (1954).

    CAS  Article  Google Scholar 

  25. 25

    Dong, D.W. & Atick, J.J. Statistics of natural time varying images. Netw. Comput. Neural Syst. 6, 345–358 (1995).

    Article  Google Scholar 

  26. 26

    Srinivasan, M.V., Laughlin, S.B. & Dubs, A. Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B 216, 427–459 (1982).

    CAS  Article  Google Scholar 

  27. 27

    Atick, J.J. Could information theory provide an ecological theory of sensory processing? Netw. Comput. Neural Syst. 3, 213–251 (1992).

    Article  Google Scholar 

  28. 28

    Dong, D.W. & Atick, J.J. Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. Netw. Comput. Neural Syst. 6, 159–178 (1995).

    Article  Google Scholar 

  29. 29

    Barlow, H.B. Possible principles underlying the transformation of sensory messages. in Sensory Communication (ed. Rosenblith, W.A.) 217–234 (MIT Press, Cambridge, Massachusetts, USA, 1961).

    Google Scholar 

  30. 30

    Schwartz, O. & Simoncelli, E.P. Natural signal statistics and sensory gain control. Nat. Neurosci. 4, 819–825 (2001).

    CAS  Article  Google Scholar 

  31. 31

    Field, D.J. What is the goal of sensory coding? Neural Comput. 6, 559–601 (1994).

    Article  Google Scholar 

  32. 32

    Willmore, B. & Tolhurst, D.J. Characterizing the sparseness of neural codes. Network 12, 255–270 (2001).

    CAS  Article  Google Scholar 

  33. 33

    Olshausen, B.A. & Field, D.J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996).

    CAS  Article  Google Scholar 

  34. 34

    Bell, A.J. & Sejnowski, T.J. The “independent components” of natural scenes are edge filters. Vision Res. 37, 3327–3338 (1997).

    CAS  Article  Google Scholar 

  35. 35

    van Hateren, J.H. & Ruderman, D.L. Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proc. R. Soc. B Biol. Sci. 265, 2315–2320 (1998).

    CAS  Article  Google Scholar 

  36. 36

    Caywood, M.S., Willmore, B. & Tolhurst, D.J. Independent components of color natural scenes resemble V1 neurons in their spatial and color tuning. J. Neurophysiol. 91, 2859–2873 (2004).

    Article  Google Scholar 

  37. 37

    Lipton, P. Testing hypotheses: prediction and prejudice. Science 307, 219–221 (2005).

    CAS  Article  Google Scholar 

  38. 38

    Hyvarinen, A., Gutmann, M. & Hoyer, P.O. Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2. BMC Neurosci. 6, 12 (2005).

    Article  Google Scholar 

  39. 39

    Karklin, Y. & Lewicki, M.S. Learning higher-order structures in natural images. Network 14, 483–499 (2003).

    Article  Google Scholar 

  40. 40

    Oppenheim, A.V. & Lim, J.S. The importance of phase in signals. Proc. IEEE. Inst. Electr. Electron. Eng. 69, 529–541 (1981).

    Article  Google Scholar 

  41. 41

    Thomson, M.G. Beats, kurtosis and visual coding. Network 12, 271–287 (2001).

    CAS  Article  Google Scholar 

  42. 42

    Wang, Z. & Simoncelli, E.P. Local phase coherence and the perception of blur. in Advances in Neural Information Processing Systems, Vol. 16 (eds. Thrun, S., Saul, L. & Scholkopf, B.) (MIT Press, Cambridge, Massachusetts, USA, 2003).

    Google Scholar 

  43. 43

    Coppola, D.M., Purves, H.R., McCoy, A.N. & Purves, D. The distribution of oriented contours in the real world. Proc. Natl. Acad. Sci. USA 95, 4002–4006 (1998).

    CAS  Article  Google Scholar 

  44. 44

    Kayser, C., Einhauser, W. & Konig, P. Temporal correlations of orientations in natural scenes. Neurocomputing 52, 117–123 (2003).

    Article  Google Scholar 

  45. 45

    Passaglia, C., Dodge, F., Herzog, E., Jackson, S. & Barlow, R. Deciphering a neural code for vision. Proc. Natl. Acad. Sci. USA 94, 12649–12654 (1997).

    CAS  Article  Google Scholar 

  46. 46

    Lewen, G.D., Bialek, W. & de Ruyter van Steveninck, R.R. Neural coding of naturalistic motion stimuli. Network 12, 317–329 (2001).

    CAS  Article  Google Scholar 

  47. 47

    Lei, Y. et al. Telemetric recordings of single neuron activity and visual scenes in monkeys walking in an open field. J. Neurosci. Methods 135, 35–41 (2004).

    Article  Google Scholar 

  48. 48

    Tanaka, K. Inferotemporal cortex and object vision. Annu. Rev. Neurosci. 19, 109–139 (1996).

    CAS  Article  Google Scholar 

  49. 49

    Fiser, J., Chiu, C. & Weliky, M. Small modulation of ongoing cortical dynamics by sensory input during natural vision. Nature 431, 573–578 (2004).

    CAS  Article  Google Scholar 

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We thank F. Han, J. Touryan, B. Willmore and W. Vinje for helpful comments. This work was supported by a grant from the National Eye Institute (R01 EY12561).

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Felsen, G., Dan, Y. A natural approach to studying vision. Nat Neurosci 8, 1643–1646 (2005). https://doi.org/10.1038/nn1608

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