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Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control

Abstract

With this paper we propose a model to simulate the functional aspects of light adaptation in retinal photoreceptors. Our model, however, does not link specific stages to the detailed molecular processes which are thought to mediate adaptation in real photoreceptors. We rather model the photoreceptor as a self-adjusting integration device, which adds up properly amplified luminance signals. The integration process and the amplification obey a switching behavior that acts to shut down locally the integration process in dependence on the internal state of the receptor. The mathematical structure of our model is quite simple, and its computational complexity is quite low. We present results of computer simulations which demonstrate that our model adapts properly to at least four orders of input magnitude.

References

  1. 1.

    Walraven J, Enroth-Cugell C, Hood D, MacLeod D, Schnapf J: The control of visual sensitivity: receptoral and postreceptoral processes. In The Neurophysiological Foundations of Visual Perception. Edited by: Spillman L, Werner J. Academic Press, New York, NY, USA; 1990:53–101.

    Google Scholar 

  2. 2.

    Barlow H, Levick W: Threshold setting by the surround of cat retinal ganglion cells. Journal of Physiology, London, B 1976, 212: 1.

    Google Scholar 

  3. 3.

    Hood D, Finkelstein M: Sensitivity to light. In Handbook of Perception and Visual Performance, Volume 1: Sensory Processes and Perception. Edited by: Boff K, Kaufman L, Thomas J. John Wiley & Sons, New York, NY, USA; 1986:5.1–5.66. chapter 5

    Google Scholar 

  4. 4.

    Mante V, Frazor RA, Bonin V, Geisler WS, Carandini M: Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience 2005,8(12):1690–1697. 10.1038/nn1556

    Article  Google Scholar 

  5. 5.

    van Hateren JH: Processing of natural time series of intensities by the visual system of the blowfly. Vision Research 1997,37(23):3407–3416. 10.1016/S0042-6989(97)00105-3

    Article  Google Scholar 

  6. 6.

    Martin G: Schematic eye models in vertebrates. Progress in Sensory Physiology 1983, 4: 44.

    Google Scholar 

  7. 7.

    Shapley R, Enroth-Cugell C: Visual adaptation and retinal gain controls. Progress in Retinal Research 1984, 3: 263–346.

    Article  Google Scholar 

  8. 8.

    Laughlin SB: The role of sensory adaptation in the retina. Journal of Experimental Biology 1989, 146: 39–62.

    Google Scholar 

  9. 9.

    Normann R, Perlman I, Hallet P: Cone photoreceptor physiology and cone contributions to colour vision. In Vision and Visual Dysfunction, The Perception of Colour. Edited by: Gouras P. Macmillan Press, London, UK; 1991:146–162.

    Google Scholar 

  10. 10.

    Barlow HB: The Ferrier Lecture, 1980. Critical limiting factors in the design of the eye and visual cortex. Proceedings of the Royal Society of London. Series B. Biological Sciences 1981,212(1186):1–34. 10.1098/rspb.1981.0022

    Article  Google Scholar 

  11. 11.

    Hood DC: Lower-level visual processing and models of light adaptation. Annual Review of Psychology 1998, 49: 503–535. 10.1146/annurev.psych.49.1.503

    Article  Google Scholar 

  12. 12.

    Meister M, Berry MJ II: The neural code of the retina. Neuron 1999,22(3):435–450. 10.1016/S0896-6273(00)80700-X

    Article  Google Scholar 

  13. 13.

    Fahrenfort I, Habets RL, Spekreijse H, Kamermans M: Intrinsic cone adaptation modulates feedback efficiency from horizontal cells to cones. Journal of General Physiology 1999,114(4):511–524. 10.1085/jgp.114.4.511

    Article  Google Scholar 

  14. 14.

    Fain GL, Matthews HR, Cornwall MC, Koutalos Y: Adaptation in vertebrate photoreceptors. Physiological Reviews 2001,81(1):117–151.

    Article  Google Scholar 

  15. 15.

    Burkhardt DA: Light adaptation and photopigment bleaching in cone photoreceptors in situ in the retina of the turtle. Journal of Neuroscience 1994,14(3 I):1091–1105.

    Article  Google Scholar 

  16. 16.

    Dowling J: The Retina: An Approachable Part of the Brain. Belknap Press/Havard University Press, Cambridge, Mass, USA; 1987.

    Google Scholar 

  17. 17.

    Kolb H, Fernandez E, Nelson R: Webvision. The organization of the vertebrate retina. 2000.https://doi.org/retina.umh.es/Webvision

    Google Scholar 

  18. 18.

    Burns ME, Baylor DA: Activation, deactivation, and adaptation in vertebrate photoreceptor cells. Annual Review of Neuroscience 2001, 24: 779–805. 10.1146/annurev.neuro.24.1.779

    Article  Google Scholar 

  19. 19.

    Grossberg S, Hong S: Cortical dynamics of surface lightness anchoring, filling-in, and perception. Journal of Vision 2003,3(9):415a.

    Google Scholar 

  20. 20.

    Hong S, Grossberg S: A neuromorphic model for achromatic and chromatic surface representation of natural images. Neural Networks 2004,17(5-6):787–808. 10.1016/j.neunet.2004.02.007

    Article  Google Scholar 

  21. 21.

    Carpenter GA, Grossberg S: Adaptation and transmitter gating in vertebrate photoreceptors. Journal of Theoretical Neurobiology 1981, 1: 1–42.

    Google Scholar 

  22. 22.

    Kamermans M, Fahrenfort I, Schultz K, Janssen-Bienhold U, Sjoerdsma T, Weiler R: Hemichannel-mediated inhibition in the outer retina. Science 2001,292(5519):1178–1180. 10.1126/science.1060101

    Article  Google Scholar 

  23. 23.

    Lamb TD: Spatial properties of horizontal cell responses in the turtle retina. Journal of Physiology 1976,263(2):239–255.

    Article  Google Scholar 

  24. 24.

    Piccolino M, Neyton J, Gerschenfeld HM:Decrease of gap junction permeability induced by dopamine and cyclic adenosine-monophosphate in horizontal cells of turtle retina. Journal of Neuroscience 1984,4(10):2477–2488.

    Article  Google Scholar 

  25. 25.

    Perona P, Malik J: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990,12(7):629–639. 10.1109/34.56205

    Article  Google Scholar 

  26. 26.

    Calvert PD, Govardovskii VI, Arshavsky VY, Makino CL: Two temporal phases of light adaptation in retinal rods. Journal of General Physiology 2002,119(2):129–145. 10.1085/jgp.119.2.129

    Article  Google Scholar 

  27. 27.

    Perry RJ, McNaughton PA: Response properties of cones from the retina of the tiger salamander. Journal of Physiology 1991,433(1):561–587.

    Article  Google Scholar 

  28. 28.

    Calvert PD, Ho TW, Lefebvre YM, Arshavsky VY: Onset of feedback reactions underlying vertebrate rod photoreceptor light adaptation. Journal of General Physiology 1998,111(1):39–51. 10.1085/jgp.111.1.39

    Article  Google Scholar 

  29. 29.

    Rebrik TI, Korenbrot JI:In intact mammalian photoreceptors,-dependent modulation of cGMP-gated ion channels is detectable in cones but not in rods. Journal of General Physiology 2004,123(1):63–75.

    Article  Google Scholar 

  30. 30.

    Vaquero CF, Pignatelli A, Partida GJ, Ishida AT: A dopamine- and protein kinase A-dependent mechanism for network adaptation in retinal ganglion cells. Journal of Neuroscience 2001,21(21):8624–8635.

    Article  Google Scholar 

  31. 31.

    Weiler R, Schultz K, Pottek M, Tieding S, Janssen-Bienhold U: Retinoic acid has light-adaptive effects on horizontal cells in the retina. Proceedings of the National Academy of Sciences of the United States of America 1998,95(12):7139–7144. 10.1073/pnas.95.12.7139

    Article  Google Scholar 

  32. 32.

    Green D, Dowling J, Siegal I, Ripps H: Retinal mechanisms of visual adaptation in the skate. The Journal of General Physiology 1975 ,65(4):483–502. 10.1085/jgp.65.4.483

    Article  Google Scholar 

  33. 33.

    Barlow HB, Levick WR: Changes in the maintained discharge with adaptation level in the cat retina. Journal of Physiology 1969,202(3):699–718.

    Article  Google Scholar 

  34. 34.

    Smirnakis SM, Berry MJ, Warland DK, Bialek W, Meister M: Adaptation of retinal processing to image contrast and spatial scale. Nature 1997,386(6620):69–73. 10.1038/386069a0

    Article  Google Scholar 

  35. 35.

    Hosoya T, Baccus SA, Meister M: Dynamic predictive coding by the retina. Nature 2005,436(7047):71–77. 10.1038/nature03689

    Article  Google Scholar 

  36. 36.

    Kawamura S: Rhodopsin phosphorylation as a mechanism of cyclic GMP phosphodiesterase regulation by S-modulin. Nature 1993,362(6423):855–857. 10.1038/362855a0

    Article  Google Scholar 

  37. 37.

    Chen C-K, Inglese J, Lefkowitz RJ, Hurley JB:-dependent interaction of recoverin with rhodopsin kinase. Journal of Biological Chemistry 1995,270(30):18060–18066. 10.1074/jbc.270.30.18060

    Article  Google Scholar 

  38. 38.

    Klenchin VA, Calvert PD, Bownds MD: Inhibition of rhodopsin kinase by recoverin. Further evidence for a negative feedback system in phototransduction. Journal of Biological Chemistry 1995,270(27):16147–16152. 10.1074/jbc.270.27.16147

    Article  Google Scholar 

  39. 39.

    Gross R, Brajovic V: An image preprocessing algorithm for illumination invariant face recognition. In Audio-and Video-Based Biometrie Person Authentication (AVBPA '03), June 2003, Guildford, UK, Springer Lecture Notes in Computer Sciences Edited by: Kittler J, Nixon M. 2688: 10–18.

    Google Scholar 

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Correspondence to Matthias S. Keil.

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Keil, M.S., Vitrià, J. Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control. EURASIP J. Adv. Signal Process. 2007, 051684 (2006). https://doi.org/10.1155/2007/51684

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Keywords

  • Information Technology
  • Computer Simulation
  • Computational Complexity
  • Quantum Information
  • Internal State