跳至主要内容

Architectural Model of a Biological Retina Using Cellular Automata

Read full paper at:
http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52679#.VKC8ecCAM4

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
 

[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.                                               eww141229lx

评论

此博客中的热门博文

A Comparison of Methods Used to Determine the Oleic/Linoleic Acid Ratio in Cultivated Peanut (Arachis hypogaea L.)

Cultivated peanut ( Arachis hypogaea L.) is an important oil and food crop. It is also a cheap source of protein, a good source of essential vitamins and minerals, and a component of many food products. The fatty acid composition of peanuts has become increasingly important with the realization that oleic acid content significantly affects the development of rancidity. And oil content of peanuts significantly affects flavor and shelf-life. Early generation screening of breeding lines for high oleic acid content greatly increases the efficiency of developing new peanut varieties. The objective of this study was to compare the accuracy of methods used to classify individual peanut seed as high oleic or not high oleic. Three hundred and seventy-four (374) seeds, spanning twenty-three (23) genotypes varying in oil composition (i.e. high oleic (H) or normal/not high oleic (NH) inclusive of all four peanut market-types (runner, Spanish, Valencia and Virginia), were individually tested ...

Location Optimization of a Coal Power Plant to Balance Costs against Plant’s Emission Exposure

Fuel and its delivery cost comprise the biggest expense in coal power plant operations. Delivery of electricity from generation to consumers requires investment in power lines and transmission grids. Placing a coal power plant or multiple power plants near dense population centers can lower transmission costs. If a coalmine is nearby, transportation costs can also be reduced. However, emissions from coal plants play a key role in worsening health crises in many countries. And coal upon combustion produces CO 2 , SO 2 , NO x , CO, Metallic and Particle Matter (PM10 & PM2.5). The presence of these chemical compounds in the atmosphere in close vicinity to humans, livestock, and agriculture carries detrimental health consequences. The goal of the research was to develop a methodology to minimize the public’s exposure to harmful emissions from coal power plants while maintaining minimal operational costs related to electric distribution losses and coal logistics. The objective was...

Evaluation of the Safety and Efficacy of Continuous Use of a Home-Use High-Frequency Facial Treatment Appliance

At present, many home-use beauty devices are available in the market. In particular, many products developed for facial treatment use light, e.g., a flash lamp or a light-emitting diode (LED). In this study, the safety of 4 weeks’ continuous use of NEWA TM , a high-frequency facial treatment appliance, every alternate day at home was verified, and its efficacy was evaluated in Japanese individuals with healthy skin aged 30 years or older who complained of sagging of the facial skin.  Transepidermal water loss (TEWL), melanin levels, erythema levels, sebum secretion levels, skin color changes and wrinkle improvement in the facial skin were measured before the appliance began to be used (study baseline), at 2 and 4 weeks after it had begun to be used, and at 2 weeks after completion of the 4-week treatment period (6 weeks from the study baseline). In addition, data obtained by subjective evaluation by the subjects themselves on a visual analog scale (VAS) were also analyzed. Fur...