1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Vision Science
  4. Volume 9, 2023
  5. Article

Annual Review of Vision Science

Volume 9, 2023

Two Sides of the Same Coin: Efficient and Predictive Neural Coding

Abstract

Some visual properties are consistent across a wide range of environments, while other properties are more labile. The efficient coding hypothesis states that many of these regularities in the environment can be discarded from neural representations, thus allocating more of the brain's dynamic range to properties that are likely to vary. This paradigm is less clear about how the visual system prioritizes different pieces of information that vary across visual environments. One solution is to prioritize information that can be used to predict future events, particularly those that guide behavior. The relationship between the efficient coding and future prediction paradigms is an area of active investigation. In this review, we argue that these paradigms are complementary and often act on distinct components of the visual input. We also discuss how normative approaches to efficient coding and future prediction can be integrated.

Keyword(s): efficient coding, information bottleneck, prediction, vision
Loading

Article metrics loading...

/content/journals/10.1146/annurev-vision-112122-020941
2023-09-15
2024-04-28
Loading full text...

Full text loading...

/deliver/fulltext/vision/9/1/annurev-vision-112122-020941.html?itemId=/content/journals/10.1146/annurev-vision-112122-020941&mimeType=html&fmt=ahah

Literature Cited

  1. Angueyra JM, Baudin J, Schwartz GW, Rieke F. 2022. Predicting and manipulating cone responses to naturalistic inputs. J. Neurosci. 42:71254–74
     [Google Scholar]
  2. Appleby TR, Manookin MB. 2019. Neural sensitization improves encoding fidelity in the primate retina. Nat. Commun. 10:4017
     [Google Scholar]
  3. Atick JJ, Redlich AN. 1990. Towards a theory of early visual processing. Neural Comput. 2:3308–20
     [Google Scholar]
  4. Atick JJ, Redlich AN. 1992. What does the retina know about natural scenes?. Neural Comput. 4:2196–210
     [Google Scholar]
  5. Attneave F. 1954. Some informational aspects of visual perception. Psychol. Rev. 61:3183–93
     [Google Scholar]
  6. Baccus SA, Meister M. 2002. Fast and slow contrast adaptation in retinal circuitry. Neuron 36:5909–19
     [Google Scholar]
  7. Barlow HB 1961. The coding of sensory messages. Current Problems in Animal Behaviour WH Thorpe, OL Zangwill 331–60. Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  8. Barlow HB, Levick WR. 1965. The mechanism of directionally selective units in rabbit's retina. J. Physiol. 178:3477–504
     [Google Scholar]
  9. Berry MJ II, Brivanlou IH, Jordan TA, Meister M 1999. Anticipation of moving stimuli by the retina. Nature 398:6725334–38
     [Google Scholar]
  10. Bialek W. 2012. Biophysics: Searching for Principles Princeton, NJ: Princeton Univ. Press
  11. Bialek W, Nemenman I, Tishby N. 2001. Predictability, complexity, and learning. Neural Comput. 13:112409–63
     [Google Scholar]
  12. Borst A, Theunissen FE. 1999. Information theory and neural coding. Nat. Neurosci. 2:11947–57
     [Google Scholar]
  13. Brenner N, Bialek W, de Ruyter van Steveninck R. 2000. Adaptive rescaling maximizes information transmission. Neuron 26:3695–702
     [Google Scholar]
  14. Campbell FW, Robson JG. 1968. Application of Fourier analysis to the visibility of gratings. J. Physiol. 197:3551–66
     [Google Scholar]
  15. Darlington TR, Beck JM, Lisberger SG. 2018. Neural implementation of Bayesian inference in a sensorimotor behavior. Nat. Neurosci. 21:101442–51
     [Google Scholar]
  16. Darlington TR, Tokiyama S, Lisberger SG. 2017. Control of the strength of visual-motor transmission as the mechanism of rapid adaptation of priors for Bayesian inference in smooth pursuit eye movements. J. Neurophysiol. 118:21173–89
     [Google Scholar]
  17. Dayan P, Hinton GE, Neal RM, Zemel RS. 1995. The Helmholtz machine. Neural Comput. 7:5889–904
     [Google Scholar]
  18. DeWeese M, Zador A. 1998. Asymmetric dynamics in optimal variance adaptation. Neural Comput. 10:51179–202
     [Google Scholar]
  19. Dong DW, Atick JJ. 1995. Statistics of natural time-varying images. Network 6:3345–58
     [Google Scholar]
  20. Fairhall AL, Lewen GD, Bialek W, de Ruyter van Steveninck RR. 2001. Efficiency and ambiguity in an adaptive neural code. Nature 412:6849787–92
     [Google Scholar]
  21. Field DJ. 1987. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4:122379–94
     [Google Scholar]
  22. Fitzgerald JE, Katsov AY, Clandinin TR, Schnitzer MJ. 2011. Symmetries in stimulus statistics shape the form of visual motion estimators. PNAS 108:3112909–14
     [Google Scholar]
  23. Friston K. 2010. The free-energy principle: a unified brain theory?. Nat. Rev. Neurosci. 11:2127–38
     [Google Scholar]
  24. Gollisch T, Meister M. 2008. Rapid neural coding in the retina with relative spike latencies. Science 319:58661108–11
     [Google Scholar]
  25. Gollisch T, Meister M. 2010. Eye smarter than scientists believed: neural computations in circuits of the retina. Neuron 65:2150–64
     [Google Scholar]
  26. Heitman A, Brackbill N, Greschner M, Sher A, Litke AM, Chichilnisky EJ. 2016. Testing pseudo-linear models of responses to natural scenes in primate retina. bioRxiv 045336. https://doi.org/10.1101/045336
  27. Hermundstad AM, Briguglio JJ, Conte MM, Victor JD, Balasubramanian V, Tkačik G. 2014. Variance predicts salience in central sensory processing. eLife 3:e03722
     [Google Scholar]
  28. Hosoya T, Baccus SA, Meister M. 2005. Dynamic predictive coding by the retina. Nature 436:704771–77
     [Google Scholar]
  29. Hu Q, Victor JD. 2010. A set of high-order spatiotemporal stimuli that elicit motion and reverse-phi percepts. J. Vis. 10:39
     [Google Scholar]
  30. Johnston J, Lagnado L. 2015. General features of the retinal connectome determine the computation of motion anticipation. eLife 4:e06250
     [Google Scholar]
  31. Kant I. 1998 (1781). Critique of Pure Reason, transl P Guyer, AW Wood Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  32. Kastner DB, Baccus SA. 2011. Coordinated dynamic encoding in the retina using opposing forms of plasticity. Nat. Neurosci. 14:101317–22
     [Google Scholar]
  33. Kastner DB, Baccus SA. 2013. Spatial segregation of adaptation and predictive sensitization in retinal ganglion cells. Neuron 79:3541–54
     [Google Scholar]
  34. Kim KJ, Rieke F. 2001. Temporal contrast adaptation in the input and output signals of salamander retinal ganglion cells. J. Neurosci. 21:1287–99
     [Google Scholar]
  35. Koch K, McLean J, Segev R, Freed MA, Berry MJ II et al. 2006. How much the eye tells the brain. Curr. Biol. 16:141428–34
     [Google Scholar]
  36. Laughlin S. 1981. A simple coding procedure enhances a neuron's information capacity. Z. Naturforsch. C 36:9–10910–12
     [Google Scholar]
  37. Lewicki MS. 2002. Efficient coding of natural sounds. Nat. Neurosci. 5:4356–63
     [Google Scholar]
  38. Lisberger SG. 2010. Visual guidance of smooth-pursuit eye movements: sensation, action, and what happens in between. Neuron 66:4477–91
     [Google Scholar]
  39. Liu B, Hong A, Rieke F, Manookin MB. 2021. Predictive encoding of motion begins in the primate retina. Nat. Neurosci. 24:91280–91
     [Google Scholar]
  40. Mani N, Huettig F. 2012. Prediction during language processing is a piece of cake—but only for skilled producers. J. Exp. Psychol. Hum. Percept. Perform. 38:4843–47
     [Google Scholar]
  41. Młynarski WF, Hermundstad AM. 2018. Adaptive coding for dynamic sensory inference. eLife 7:e32055
     [Google Scholar]
  42. Młynarski WF, Hermundstad AM. 2021. Efficient and adaptive sensory codes. Nat. Neurosci. 24:7998–1009
     [Google Scholar]
  43. Nikolaev A, Leung K-M, Odermatt B, Lagnado L. 2013. Synaptic mechanisms of adaptation and sensitization in the retina. Nat. Neurosci. 16:7934–41
     [Google Scholar]
  44. Nitzany EI, Victor JD. 2014. The statistics of local motion signals in naturalistic movies. J. Vis. 14:410
     [Google Scholar]
  45. Olshausen BA, Field DJ. 1996. Natural image statistics and efficient coding. Network 7:2333–39
     [Google Scholar]
  46. Oppenheim AV, Lim JS. 1981. The importance of phase in signals. Proc. IEEE 69:5529–41
     [Google Scholar]
  47. Palmer SE, Marre O, Berry MJ II, Bialek W 2015. Predictive information in a sensory population. PNAS 112:226908–13
     [Google Scholar]
  48. Piotrowski LN, Campbell FW. 1982. A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11:3337–46
     [Google Scholar]
  49. Pitkow X, Meister M. 2012. Decorrelation and efficient coding by retinal ganglion cells. Nat. Neurosci. 15:4628–35
     [Google Scholar]
  50. Rao RP, Ballard DH. 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2:179–87
     [Google Scholar]
  51. Rieke F. 2001. Temporal contrast adaptation in salamander bipolar cells. J. Neurosci. 21:239445–54
     [Google Scholar]
  52. Rieke F, Bodnar DA, Bialek W. 1995. Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents. Proc. Biol. Sci. 262:1365259–65
     [Google Scholar]
  53. Ruderman DL, Bialek W. 1994. Statistics of natural images: scaling in the woods. Phys. Rev. Lett. 73:6814–17
     [Google Scholar]
  54. Rust NC, Palmer SE. 2021. Remembering the past to see the future. Annu. Rev. Vis. Sci. 7:349–65
     [Google Scholar]
  55. Sabbah S, Gemmer JA, Bhatia-Lin A, Manoff G, Castro G et al. 2017. A retinal code for motion along the gravitational and body axes. Nature 546:7659492–97
     [Google Scholar]
  56. Salisbury JM, Palmer SE. 2016. Optimal prediction in the retina and natural motion statistics. J. Stat. Phys. 162:51309–23
     [Google Scholar]
  57. Schwartz G, Harris R, Shrom D, Berry MJ II. 2007a. Detection and prediction of periodic patterns by the retina. Nat. Neurosci. 10:5552–54
     [Google Scholar]
  58. Schwartz G, Taylor S, Fisher C, Harris R, Berry MJ II. 2007b. Synchronized firing among retinal ganglion cells signals motion reversal. Neuron 55:6958–69
     [Google Scholar]
  59. Shannon CE. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27:3379–423
     [Google Scholar]
  60. Sharpee TO, Sugihara H, Kurgansky AV, Rebrik SP, Stryker MP, Miller KD. 2006. Adaptive filtering enhances information transmission in visual cortex. Nature 439:7079936–42
     [Google Scholar]
  61. Simoncelli EP, Olshausen BA. 2001. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24:1193–216
     [Google Scholar]
  62. Smirnakis SM, Berry MJ, Warland DK, Bialek W, Meister M. 1997. Adaptation of retinal processing to image contrast and spatial scale. Nature 386:662069–73
     [Google Scholar]
  63. Smith EC, Lewicki MS. 2006. Efficient auditory coding. Nature 439:7079978–82
     [Google Scholar]
  64. Srinivasan MV, Laughlin SB, Dubs A. 1982. Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B 216:1205427–59
     [Google Scholar]
  65. Stocker AA, Simoncelli EP. 2006. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci. 9:4578–85
     [Google Scholar]
  66. Stone LS, Thompson P. 1992. Human speed perception is contrast dependent. Vis. Res. 32:81535–49
     [Google Scholar]
  67. Taylor WR, He S, Levick WR, Vaney DI. 2000. Dendritic computation of direction selectivity by retinal ganglion cells. Science 289:54882347–50
     [Google Scholar]
  68. Thompson P. 1982. Perceived rate of movement depends on contrast. Vis. Res. 22:3377–80
     [Google Scholar]
  69. Tikidji-Hamburyan A, Reinhard K, Seitter H, Hovhannisyan A, Procyk CA et al. 2015. Retinal output changes qualitatively with every change in ambient illuminance. Nat. Neurosci. 18:166–74
     [Google Scholar]
  70. Tishby N, Pereira FC, Bialek W. 1999. The information bottleneck method. Proceedings of the 37th Annual Allerton Conference on Communication, Control and Computing368–77. Champaign, IL: Univ. Ill.
     [Google Scholar]
  71. Turner MH, Rieke F. 2016. Synaptic rectification controls nonlinear spatial integration of natural visual inputs. Neuron 90:61257–71
     [Google Scholar]
  72. Turner MH, Sanchez Giraldo LG, Schwartz O, Rieke F. 2019. Stimulus- and goal-oriented frameworks for understanding natural vision. Nat. Neurosci. 22:115–24
     [Google Scholar]
  73. van Hateren JH. 1992. A theory of maximizing sensory information. Biol. Cybern. 68:123–29
     [Google Scholar]
  74. von Helmholtz H. 2000 (1867). Concerning the Perceptions in General, Vol. 3 transl. JPC Southall Bristol, UK: Thoemmes Press
  75. Wark B, Fairhall A, Rieke F. 2009. Timescales of inference in visual adaptation. Neuron 61:5750–61
     [Google Scholar]
  76. Weiss Y, Simoncelli EP, Adelson EH. 2002. Motion illusions as optimal percepts. Nat. Neurosci. 5:6598–604
     [Google Scholar]
  77. Yang J, Lee J, Lisberger SG. 2012. The interaction of Bayesian priors and sensory data and its neural circuit implementation in visually guided movement. J. Neurosci. 32:4917632–45
     [Google Scholar]
/content/journals/10.1146/annurev-vision-112122-020941
Loading
Two Sides of the Same Coin: Efficient and Predictive Neural Coding
Annual Review of Vision Science 9, 293 (2023); https://doi.org/10.1146/annurev-vision-112122-020941
/content/journals/10.1146/annurev-vision-112122-020941
/content/journals/10.1146/annurev-vision-112122-020941
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error