Abstract
The retina performs various key operations on incoming images in order to facilitate higher-level visual processing. Since the retina outperforms existing image enhancing techniques, it follows that computational simulations with biological plausibility are best suited to inform their design and development, as well as help us better understand retina functionality. Recently, it has been determined that quality of vision is dependant on the interaction between rod and cone pathways, traditionally thought to be wholly autonomous. This interaction improves the signal-to-noise ratio (SNR) within the retina and in turn enhances boundary detection by cones. In this paper we therefore propose the first cone simulator that incorporates input from rods. Our results show that rod-cone convergence does improve SNR, therefore allowing for improved contrast sensitivity, and consequently visual perception.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arici, T., Altunbasak, Y.: Image local contrast enhancement using adaptive non-linear filters. In: IEEE International Conference on Image Processing, pp. 2881–2884 (2006)
Baylor, D.: How photons start vision. Proceedings of the National Academy of Sciences 93(2), 560–565 (1996)
Graf, H.G., Dollberg, A., Spüntrup, J.D., Warkentin, K.: HDR Sub-retinal Implant for the Vision Impaired High-Dynamic-Range (HDR) Vision. Springer Series in Advanced Microelectronics, vol. 26, pp. 141–146. Springer, Heidelberg (2007)
Hood, D.C.: Lower-level visual processing and models of light adaptation. Annual Review of Psychology 49, 503–535 (1998)
Hood, D., Birch, D.: Human cone receptor activity: The leading edge of the a–wave and models of receptor activity. Visual Neuroscience 10(05), 857–871 (1993)
Macknik, S.L., Livingstone, M.S.: Neuronal correlates of visibility and invisibility in the primate visual system. Nature Neuroscience 1(2), 144 (1998)
Mantiuk, R., Myszkowski, K., Seidel, H.P.: A perceptual framework for contrast processing of high dynamic range images. ACM Trans. Appl. Percept. 3(3), 286–308 (2006), http://doi.acm.org/10.1145/1166087.1166095
Schiller, P.H.: Parallel information processing channels created in the retina. Proceedings of the National Academy of Sciences (2010)
Schnapf, J., Nunn, B., Baylor, D.: Visual transduction in cones of the monkey macaca fascicularis 427(1), 681–713 (1990)
Shah, S., Levine, M.D.: Visual information processing in primate cone pathways. i. a model. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(2), 259–274 (1996)
Shapley, R., Enroth-Cugell, C.: Visual adaptation and retinal gain controls, vol. 3, ch. 9, pp. 263–346. Pergamon (1984)
Trümpler, J., Dedek, K., Schubert, T., de Sevilla Müller, L.P., Seeliger, M., Humphries, P., Biel, M., Weiler, R.: Rod and cone contributions to horizontal cell light responses in the mouse retina. Journal of Neuroscience 28(27), 6818–6825 (2008)
Wilson, H.R.: A neural model of foveal light adaptation and afterimage formation. Visual Neuroscience 14(03), 403–423 (1997)
Wohrer, A., Kornprobst, P.: Virtual retina: A biological retina model and simulator, with contrast gain control. Journal of Computational Neuroscience 26(2), 219–249 (2009)
Yau, K.: Phototransduction mechanism in retinal rods and cones: The friedenwald lecture. Investigative Ophthalmology and Visual Science 35(1), 9–32 (1994)
Zaghloul, K.A., Boahen, K.: A silicon retina that reproduces signals in the optic nerve. Journal of Neural Engineering 3(4), 257–267 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muchungi, K., Casey, M. (2012). Simulating Light Adaptation in the Retina with Rod-Cone Coupling. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-33269-2_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
Online ISBN: 978-3-642-33269-2
eBook Packages: Computer ScienceComputer Science (R0)