Skip to main content
SpringerLink
Log in

The binocular neural mechanism: disparity coding schemes and population coding

立体视觉的神经机制: 视差编码与群编码

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Stereoacuity thresholds measured on disparity pedestals are generally found to increase exponentially as the pedestals move away from horopters. However, Farell, Li, and McKee recently found that for sinusoidal stimuli this threshold function had a dip—a pedestal effect. This paper examines the underlying neural mechanism. We suggest a general disparity coding scheme with two position parameters, which are necessary to account for the phenomenon that the response of the energy model depends on the absolute phase of the stimulus. This scheme was implemented to simulate the responses, calculated from an energy model, of the neurons of a full V1 cortical column. To explain the stereo pedestal effect, we propose a decoding mechanism, which is first processed along the phase dimension and then along the orientation dimension. The final step of the decoding mechanism, probability summation over the outputs of spatial frequency channels, yields the dip, producing a disparity increment threshold function similar to the psychophysical result.

抽象

创新点

在立体视觉差基底上测量的敏度阈值一般是随着基底离开同视点而呈幂指数增长. 但 是法诺等人发现当使用正弦图象时阈值函数有一勺形凹陷 - 基底效应. 本文试图检查这一现象 下的神经机制. 我们提出了一个有两个参数的一般视差编码机制. 为了解释立体视觉的基底效应, 我们提出一种解码机制. 该机制使我们最后生成勺状的视差增值阈值函数.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wheatstone C. Contributions to the physiology of vision—Part the first. On some remarkable, and hithero unobserved, phenomena of binocular vision. Phil Trans Roy Soc, 1838, 128: 371–394

    Article  Google Scholar 

  2. Badcock R, Schor C. Depth-increment detection function for individual spatial channels. J Opt Soc Am A-Opt Image Sci, 1985, 2: 1211–1216

    Article  Google Scholar 

  3. McKee S P, Levi D M, Bowne S F. The imprecision of stereopsis. Vis Res, 1990, 30: 1763–1779

    Article  Google Scholar 

  4. Schumer R A, Julesz B. Binocular disparity modulation sensitivity to disparities offset from plane of fixation. Vis Res, 1984, 24: 533–542

    Article  Google Scholar 

  5. Lehky S R, Sejnowski T J. Neural model of stereoacuity and depth interpolation based on a distributed representation of stereo disparity. J Neurosci, 1990, 10: 2281–2299

    Google Scholar 

  6. Poggio G F, Fischer B. Binocular interaction and depth sensitivity in striate and prestriate cortex of behaving rhesus monkey. J Neurophysiol, 1977, 40: 1392–1405

    Google Scholar 

  7. Pelli D G. Uncertainty explains many aspects of visual contrast detection and distribution. J Opt Soc Am A-Opt Image Sci, 1985, 2: 1508–1531

    Article  Google Scholar 

  8. Farell B, Li S, McKee S P. Disparity increment thresholds for gratings. J Vision, 2004, 4: 156–168

    Article  Google Scholar 

  9. McKee S P, Verghese P, Farell B. What is the depth of a sinusoidal grating? J Vision, 2004, 4: 524–538

    Article  Google Scholar 

  10. Rohaly A M, Wilson H R. Nature of coarse-to-fine constraints on binocular fusion. J Opt Soc Am A-Opt Image Sci, 1993, 10: 2433–2441

    Article  Google Scholar 

  11. Schor C M, Badcock D R. A comparison of stereo and vernier acuity within spatial channels as a function of distance from fixation. Vis Res, 1985, 25: 1113–1119

    Article  Google Scholar 

  12. Smallman H S, MacLeod D I A. Size-disparity correlation in stereopsis at contrast threshold. J Opt Soc Am A-Opt Image Sci, 1994, 11: 2169–2183

    Article  Google Scholar 

  13. Georgeson M A, Yates T A, Schofield A J. Discriminating depth in corrugated stereo surfaces: facilitation by a pedestal is explained by removal of uncertainty. Vis Res, 2008, 48: 2321–2328

    Article  Google Scholar 

  14. Legge G E, Foley J M. Contrast masking in human vision. J Opt Soc Am A-Opt Image Sci, 1980, 70: 1458–1471

    Article  Google Scholar 

  15. Huang P C, Chen C C. A comparison of pedestal effects in first- and second-order patterns. J Vision, 2014, 14: 1–15

    Google Scholar 

  16. Kilpelainen M, Nurminen L, Donner K. The effect of mean luminance change and grating pedestals on contrast perception: model simulation suggest a common, retinal, origin. Vis Res, 2012, 58: 51–58

    Article  Google Scholar 

  17. Graham N V S. Visual Pattern Analyzers. Oxford: Oxford Press, 1989. 400–402

    Book  Google Scholar 

  18. Ohzawa I, DeAngelis G C, Freeman R D. Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science, 1990, 249: 1037–1041

    Article  Google Scholar 

  19. Cumming B G, DeAngelis G C. The physiology of stereopsis. Annu Rev Neurosci, 2001, 24: 203–238

    Article  Google Scholar 

  20. Daugman J G. Two-dimensional spectral analysis for cortical receptive field profiles. Vis Res, 1980, 20: 847–856

    Article  Google Scholar 

  21. Daugman J G. Six formal properties of two-dimensional anisotropic visual filters: structural principles and frequency/ orientation selectivity. IEEE Trans Syst Man Cybern, 1983, 13: 882–887

    Article  Google Scholar 

  22. Daugman J G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by twodimensional visual cortical filters. J Opt Soc Am A-Opt Image Sci, 1985, 2: 1160–1169

    Article  Google Scholar 

  23. Daugman J G. Complete discrete 2-D gabor transforms by neural networks for image analysis and compression. IEEE Trans Acoust Speech Sign, 1988, 36: 1169–1179

    Article  Google Scholar 

  24. de Valois R L, Albrecht D G, Thorell L G. Spatial frequency selectivity of cells in macaque visual cortex. Vis Res, 1982, 22: 545–559

    Article  Google Scholar 

  25. Fleet D J, Wagner H, Heeger D J. Neural encoding of binocular disparity: energy models, position shifts and phase shifts. Vis Res, 1996, 36: 1839–1857

    Article  Google Scholar 

  26. Schor C. Binocular vision. In: de Valois K K, ed. Seeing: Handbook of Perception and Cognition. 2nd ed. San Diego: Academic Press, 2000. 177–257

    Chapter  Google Scholar 

  27. Barlow H B, Blakemore C, Pettigrew J D. The neural mechanism of binocular depth discrimination. J Physiol, 1967, 193: 327–342

    Article  Google Scholar 

  28. DeAngelis G C, Ohzawa I, Freeman R D. Depth is encoded in the visual cortex by a specialized receptive structure. Nature, 1991, 352: 156–159

    Article  Google Scholar 

  29. Freeman R D, Ohzawa I. On the neurophysiological organization of binocular vision. Vis Res, 1990, 30: 1661–1676

    Article  Google Scholar 

  30. Nomura M, Matsumoto G, Fujiwara S. A binocular model for the simple cell. Biol Cybern, 1990, 63: 237–242

    Article  Google Scholar 

  31. Marr D, Poggio T. A computational theory of human stereo vision. Proc Roy Soc London Ser B, 1979, 204: 301–328

    Article  Google Scholar 

  32. Schor C M, Wood I. Disparity range for local stereopsis as a function of luminance spatial frequency. Vis Res, 1983, 23: 1649–1654

    Article  Google Scholar 

  33. Anzai A, Ohzawa I, Freeman R D. Neural mechanisms underlying binocular fusion and stereopsis: position vs. phase. Proc Nat Acad Sci USA, 1997, 94: 5438–5443

    Article  Google Scholar 

  34. Anzai A, Ohzawa I, Freeman R D. Neural mechanisms for binocular disparity: receptive field position versus phase. J Neurophysiol, 1999, 82: 874–890

    Google Scholar 

  35. Livingstone M S, Tsao D Y. Receptive fields of disparity-selective neurons in macaque striate cortex. Nat Neurosci, 1999, 2: 825–832

    Article  Google Scholar 

  36. Prince S J D, Pointon A D, Cumming B G, et al. Range and mechanism of encoding of horizontal disparity in macaque V1. J Neurophysiol, 2002, 87: 209–221

    Google Scholar 

  37. Prince S J D, Pointon A D, Cumming B G, et al. Quantitative analysis of the responses of V1 neurons to horizontal disparity in dynamic random-dot stereograms. J Neurophysiol, 2002, 87: 191–208

    Google Scholar 

  38. Qian N, Zhu Y D. Physiological computation of binocular disparity. Vis Res, 1997, 37: 1811–1827

    Article  Google Scholar 

  39. Blake R, Wilson H. Binocular vision. Vis Res, 2011, 51: 754–770

    Article  Google Scholar 

  40. Chen Y, Qian N. A coarse-to-fine disparity energy model with both phase-shift and position-shift receptive field mechanisms. Neural Comput, 2004, 16: 1545–1577

    Article  Google Scholar 

  41. Assee A, Qian N. Sloving da Vinci stereopsis with depth-edge-selective V2 cells. Vis Res, 2007, 51: 2585–2602

    Article  Google Scholar 

  42. Read J C, Cumming B. Sensors for impossible stimuli may solve the stereo correspondence problem. Nat Neurosci, 2007, 10: 1322–1328

    Article  Google Scholar 

  43. Howard I P, Rogers B J. Binocular Vision and Stereopsis. Oxford: Oxford Press, 1995. 235–312

    Google Scholar 

  44. Bridge H, Cumming B G. Responses of macaque V1 neurons to binocular orientation difference. J Neurosci, 2001, 21: 7293–7302

    Google Scholar 

  45. Gazzaniga M S, Ivery R B, Mangun G R. Cognitive Neuroscience: the Biology of Brain. New York: W W Norton Co. Inc., 1998. 461–469

    Google Scholar 

  46. Pouget A, Dayan P, Zemel R. Information processing with population codes. Nat Rev Neurosci, 2000, 1: 125–132

    Article  Google Scholar 

  47. Lee T S. Image representation using 2D gabor wavelets. IEEE Trans Patt Anal Mach Intell, 1996, 18: 1–13

    Article  Google Scholar 

  48. Ng J, Bharath A A, Li Z. A survey of architecture and function of primary visual cortex. EURASIP J Adv Signal Process, 2007, 1: 221–245

    Google Scholar 

  49. Morrone M C, Burr D C. Feature detection in human vision: a phase-depend energy model. Proc Roy Soc London Ser B, 235: 1–17

  50. Zhao L, Farell B. The absolute phase effect of the energy model. J Vision, 2005, 5: 255–255

    Article  Google Scholar 

  51. Zhao L, Farell B. The binocular neural mechanism: gnostic and population coding. J Vision, 2002, 2: 312–312

    Article  Google Scholar 

  52. Adelson E H, Bergen J. Spatialtemporal energy model for the perception of motion. J Opt Soc Am A-Opt Image Sci, 1985, 2: 284–199

    Article  Google Scholar 

  53. Regan D, Beverley K. Spatial frequency discrimination and detection: comparison of post-adaptation thresholds. J Opt Soc Am A-Opt Image Sci, 1983, 73: 1684–1690

    Article  Google Scholar 

  54. Regan D, Beverley, K. Postadaption orientation discrimination. J Opt Soc Am A-Opt Image Sci, 1985, 2: 147–155

    Article  Google Scholar 

  55. Wilson H R. Responses of spatial mechanisms can explain hyperacuity. Vis Res, 1986, 26: 453–469

    Article  Google Scholar 

  56. Purushothaman G, Bradley D C. Neural population code for fine perceptual decisions in area MT. Nat Neurosci, 2005, 8: 99–106

    Article  Google Scholar 

  57. Wilson H R. Spikes, Decisions and Actions: Dynamical Foundations of Neuroscience. Oxford: Oxford Press, 1999. 88–92

    Google Scholar 

  58. Oppenheim A V, Willsky A S, Nawab S H. Signals and Systems. Upper Saddle River: Prentice Hall, 1997. 590–594

    Google Scholar 

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

    Article  Google Scholar 

  60. Carandini M, Heeger D J, Movhson J A. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci, 1997, 17: 8621–8644

    Google Scholar 

  61. Green D M, Swets J A. Signal Detection Theory and Psychophysics. Melbourne: Krieger, 1966. 45–51

    Google Scholar 

  62. Li Z P, Atick J J. Efficient stereo coding in the multiscale representation. Netw Comput Neural Syst, 1994, 5: 157–174

    Article  Google Scholar 

  63. Qian N. Computing stereo disparity and motion with known binocular cell properties. Neural Comput, 1994, 6: 390–404

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Engineering Institute, Henan University of Science and Technology, Luoyang, 471023, China

    Li Zhao

Authors
  1. Li Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, L. The binocular neural mechanism: disparity coding schemes and population coding. Sci. China Inf. Sci. 58, 1–14 (2015). https://doi.org/10.1007/s11432-014-5257-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-014-5257-7

Keywords

关键词

Search

Navigation