Back
 JCC  Vol.2 No.14 , December 2014
Architectural Model of a Biological Retina Using Cellular Automata
Abstract: Developments in neurophysiology focusing on foveal vision have characterized more and more precisely the spatiotemporal processing that is well adapted to the regularization of the visual information within the retina. The works described in this article focus on a simplified architectural model based on features and mechanisms of adaptation in the retina. Similarly to the biological retina, which transforms luminance information into a series of encoded representations of image characteristics transmitted to the brain, our structural model allows us to reveal more information in the scene. Our modeling of the different functional pathways permits the mapping of important complementary information types at abstract levels of image analysis, and thereby allows a better exploitation of visual clues. Our model is based on a distributed cellular automata network and simulates the retinal processing of stimuli that are stationary or in motion. Thanks to its capacity for dynamic adaptation, our model can adapt itself to different scenes (e.g., bright and dim, stationary and moving, etc.) and can parallelize those processing steps that can be supported by parallel calculators.
Cite this paper: Devillard, F. and Heit, B. (2014) Architectural Model of a Biological Retina Using Cellular Automata. Journal of Computer and Communications, 2, 78-97. doi: 10.4236/jcc.2014.214008.
References

[1]   Masland, R.H. (2001) Neuronal Diversity in the Retina. Current Opinion in Neurobiology, 11, 431-436.
http://dx.doi.org/10.1016/S0959-4388(00)00230-0

[2]   Masland, R.H. (2001) The Fundamental Plan of the Retina. Nature Neuroscience, 4, 877-886.
http://dx.doi.org/10.1038/nn0901-877

[3]   Frisby, J.P. and Stone, J.V. (2010) Seeing: The Computational Approach to Biological Vision. 2nd Edition. The MIT Press.

[4]   Wolfram, S. (2002) A New Kind of Science. 1st Edition, Wolfram Media Inc., Champaign, 955.

[5]   Beigzadeh, M., Golpayegani, S.M.R.H. and Gharibzadeh, S. (2013) Can Cellular Automata Be a Representative Model for Visual Perception Dynamics? Frontiers in Computational Neuroscience, 7, 1-2.

[6]   Marr, D. (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Company, San Francisco.

[7]   Mead, C. (1989) Analog VLSI and Neural Systems. Addison-Wesley, Upper Saddle River.
http://dx.doi.org/10.1007/978-1-4613-1639-8

[8]   Masland, R.H. (2012) The Neuronal Organization of the Retina. Neuron, 76, 266-280.
http://dx.doi.org/10.1016/j.neuron.2012.10.002

[9]   Swaroop, A., Kim, D. and Forrest, D. (2010) Transcriptional Regulation of Photoreceptor Development and Homeostasis in the Mammalian Retina. Neuroscience, 11, 563-576.

[10]   Rigaudière, F., Le Gargasson, J.F. and Delouvrier, E. (2010) IV-Les voies visuelles: Rappels anatomo-fonctionnels. (Eil et physiologie de la vision, IV-Les voies visuelles, 209-262.

[11]   Hartline, H.K. (1940) The Effects of Spatial Summation in the Retina on the Excitation of the Fibers of the Optic Nerve. American Journal of Physiology, 130, 700-711.

[12]   Blythe, S.N. and Krantz, J.H. (2004) A Mathematical Model of Retinal Receptive Fields Capable of Form & Color Analysis. Impulse: The Premier Journal for Undergraduate Publications in the Neurosciences, 1, 38-50.

[13]   Hashimoto, T., Katai, S., Saito, Y., Kobayashi, F. and Goto, T. (2012) ON and OFF Channels in Human Retinal Ganglion Cells. The Journal of Physiology, 591, 327-337.

[14]   Protti, D.A., Di Marco, S., Huang, J.Y., Vonhoff, C.R., Nguyen, V. and Solomon, S.G. (2014) Inner Retinal Inhibition Shapes the Receptive Field of Retinal Ganglion Cells in Primate. Journal of Physiology, 592, 49-65.

[15]   Dowling, J.E. (1987) The Retina: An Approach Part of the Brain. 2nd Edition, Belknap Press of Harvard University Press, Cambridge.

[16]   Wohrer, A., Kornprobst, P. and Vieville, T. (2006) A Biologically-Inspired Model for a Spiking Retina. Technical Report 5848, INRIA.

[17]   Muchungi, K. and Casey, M.C. (2012) Simulating Light Adaptation in the Retina with Rod-Cone Coupling. Proceedings of the 22nd International Conference on Artificial Neural Networks, Lausanne, 11-14 September 2012, 339-346. http://epubs.surrey.ac.uk/723403

[18]   Mustafi, D., Engel, A.H. and Palczewski, K. (2009) Structure of Cone Photoreceptors. Progress in Retinal and Eye Research, 28, 289-302. http://dx.doi.org/10.1016/j.preteyeres.2009.05.003

[19]   Van Hateren, J.H. (2007) A Model of Spatiotemporal Signal Processing by Primate Cones and Horizontal Cells. Journal of Vision, 7, 1-19. http://dx.doi.org/10.1167/7.4.1

[20]   Dacey, D., Packer, O.S., Diller, L., Brainard, D., Peterson, B. and Lee, B. (2000) Center Surround Receptive Field Structure of Cone Bipolar Cells in Primate Retina. Vision Research, 40, 1801-1811.
http://dx.doi.org/10.1016/S0042-6989(00)00039-0

[21]   Demb, J.B., Haarsma, L., Freed, M.A. and Sterling, P. (1999) Functional Circuitry of the Retinal Ganglion Cell’s Nonlinear Receptive Field. The Journal of Neuroscience, 19, 9756-9767.

[22]   Zhang, A.J. and Wu, S.M. (2009) Receptive Fields of Retinal Bipolar Cells Are Mediated by Heterogeneous Synaptic Circuitry. The Journal of Neuroscience, 29, 789-797.
http://dx.doi.org/10.1523/JNEUROSCI.4984-08.2009

[23]   MacNeil, M.A. and Masland, R.H. (1998) Extreme Diversity among Amacrine Cells: Implications for Function. Neuron, 20, 971-982. http://dx.doi.org/10.1016/S0896-6273(00)80478-X

[24]   Hsueh, H.A., Molnar, A. and Werblin, F.S. (2008) Amacrine-to-Amacrine Cell Inhibition in the Rabbit Retina. Journal of Neurophysiology, 100, 2077-2088. http://dx.doi.org/10.1152/jn.90417.2008

[25]   Brown, S., He, S. and Masland, R.H. (2000) Receptive Field Microstructure and Dendritic Geometry of Retinal Ganglion Cells. Neuron, 27, 71-383. http://dx.doi.org/10.1016/S0896-6273(00)00044-1

[26]   Curcio, C.A. and Allen, K.A. (1990) Topography of Ganglion Cells in Human Retina. The Journal of Comparative Neurology, 300, 5-25. http://dx.doi.org/10.1002/cne.903000103

[27]   Demb, J.B., Zaghloul, K., Haarsma, L. and Sterling, P. (2001) Bipolar Cells Contribute to Nonlinear Spatial Summation in the Brisk-Transient (Y) Ganglion Cell in Mammalian Retina. The Journal of Neuroscience, 21, 7447-7454.

[28]   Geffen, M.N., de Vries, S.E. and Meister, M. (2007) Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On. PLoS Biology, 5, e65.

[29]   Li, Z.P. (1992) Different Retinal Ganglion Cells have Different Functional Goals. International Journal of Neural Systems, 3, 237-248.

[30]   Wohrer, A. and Kornprobst, P. (2009) Virtual Retina: A Biological Retina Model and Simulator, with Contrast Gain Control. Journal of Computer Neuroscience, 26, 219-249.
http://dx.doi.org/10.1007/s10827-008-0108-4

[31]   Beaudot, W.H.A., Oliva, A. and Herault, J. (1995) Retinal Model of the Dynamics of X and Y Pathways: A Neural Basis for Early Coarse-to-Fine Perception. Proceedings of the European Conference on Visual Perception, Tuebingen, 21-25 August 1995, 93b.

[32]   Beaudot, W.H.A. (1994) Le traitement neuronal de l’information dans la rétine des vertébrés—Un creuset d’idées pour la vision artificielle. Ph.D. Thesis, Institut National Polytechnique de Grenoble, Grenoble.

[33]   Adelman, T.L., Bialek, W. and Olberg, R.M. (2003) The Information Content of Receptive Fields. Neuron, 40, 823-833. http://dx.doi.org/10.1016/S0896-6273(03)00680-9

[34]   Conway, J.H. (1970) Game of Life. Scientific American, 223, 120-123.

[35]   Packard, N.H. and Wolfram, S. (1985) Two-Dimensional Cellular Automata. Journal of Statistical Physics, 38, 901-946.

[36]   Alber, M.S., et al. (2002) On Cellular Automaton Approaches to Modeling Biological Cells. In: Rosenthal, J. and Gilliam, D.S., Eds., IMA Mathematical Systems Theory in Biology, Communication and Finance, Springer-Verlag, Berlin.

[37]   Chauhan, S. (2013) Survey Paper on Training of Cellular Automata for Image. International Journal of Engineering and Computer Science, 2, 980-985.

[38]   Gonzalez, R.C. and Woods, R.E. (1989) Digital Image Processing. 3rd Edition, Prentice Hall, Englewood Cliff.

[39]   Richefeu, J.C. and Manzanera, A. (2004) A New Hybrid Differential Filter for Motion Detection. Computer Vision and Graphics, 28, 727-732.

[40]   Pitas, I. and Venetsanopoulos, A.N. (1992) Order Statistics in Digital Image Processing. Proceedings of the IEEE, 80, 1893-1921. http://dx.doi.org/10.1109/5.192071

[41]   Lee, B.B., Dacey, D.M., Smith, V.C. and Pokorny, J. (1999) Horizontal Cells Reveal Cone Type-Specific Adaptation in Primate Retina. Proceedings of the National Academy of Sciences of United States of America, 96, 14611-14616.

[42]   Shapley, R. and Enroth-Cugell, C. (1984) Visual Adaptation and Retinal Gain Controls. Progress in Retinal Research, 3, 263-346.

[43]   Meylan, L., Alleysson, D. and Süsstrunk, S. (2007) A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images. Journal of the Optical Society of America A, 24, 2807-2816.

[44]   Benoit, A., Caplier, A., Durette, B. and Herault, J. (2010) Using Human Visual System Modeling for Bio-Inspired Low Level Image Processing. Computer Vision and Image Understanding, 114, 758-773.

[45]   Naka, K.I. and Rushton, W.A.H. (1966) S-Potential from Luminosity Units in the Retina of Fish (Cyprinidae). Journal of Physiology, 185, 587-599.

[46]   Buntain, C. (2012) Psychophysics and Just-Noticeable Difference CMSC828D Report 4.
http://www.cs.umd.edu/class/fall2012/cmsc828d/\\reportfiles/buntain4.pdf

[47]   Beaudot, W.H.A. (1993) The Vertebrate Retina: A Model of Spatiotemporal Image Filtering. In: GRETSI'93, XIVème GRETSI Conférence, Juan-les-Pins, 427-430.

[48]   Kauffmann, C. and Piché, N. (2009) A Cellular Automaton Framework for Image Processing on GPU. Pattern Recoginition, 353-375.

[49]   Gobron, S., Devillard, F. and Heit, B. (2006) Retina Simulation Using Cellular Automaton and GPU Programming. Machine Vision and Applications, 18, 331-342.
http://dx.doi.org/10.1007/s00138-006-0065-8

[50]   Khan, A.R. (2010) On Two Dimensional Cellular Automata and Its VLSI Applications. International Journal of Electrical & Computer Sciences, 10, 111-114.

 
 
Top