Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Free Task

Hong Hu, Liang Pang, Dongping Tian, Zhongzhi Shi

Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

DOI: 10.4236/ijis.2013.34016                

Abstract

Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 10⁶ neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 10⁸ neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.

Keywords

Artificial Brain Research; Brain-Like Computer; Fuzzy Logic; Neural Network; Machine Learning; Hopfield Neural Network; Bounded Fuzzy Operator

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	H. De Garis, C. Shuo, B. Goertzel and L. Ruiting, “A World Survey of Artificial Brain Projects, Part 1: Large-Scale Brain Simulations,” Neurocomputing, Vol. 74, No. 1, 2010, pp. 3-29. http://dx.doi.org/10.1016/j.neucom.2010.08.004
[2]	M. Djurfeldt, M. Lundqvist, C. Johansson, M. Rehn, O. Ekeberg and A. Lansner, “Brain-Scale Simulation of the Neocortex on the Ibm Blue Gene/l Supercomputer,” IBM Journal of Research and Development, Vol. 52, No. 1-2, 2008, pp. 31-41.
[3]	C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, C. Tang and D. Rasmussen, “A Large-Scale Model of the Functioning Brain,” Science, Vol. 338, No. 6111, 2012, pp. 1202-1205.
[4]	J. O’Kusky and M. Colonnier, “A Laminar Analysis of the Number of Neurons, Glia, and Synapses in the Visual Cortex (Area 17) of Adult Macaque Monkeys,” Journal of Comparative Neurology, Vol. 210, No. 3, 1982, pp. 278-290. http://dx.doi.org/10.1002/cne.902100307
[5]	K. Hirota and W. Pedrycz, “Or/And Neuron in Modeling Fuzzy Set Connectives,” IEEE Transactions on Fuzzy Systems, Vol. 2, No. 2, 1994, pp. 151-161. http://dx.doi.org/10.1109/91.277963
[6]	W. Pedrycz and F. Gomide, “An Introduction to Fuzzy Sets: Analysis and Design,” The MIT Press, Massachusetts, 1998.
[7]	L. Zhaoping, “Pre-Attentive Segmentation and Correspondence in Stereo,” Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, Vol. 357, No. 1428, 2002, pp. 1877-1883. http://dx.doi.org/10.1098/rstb.2002.1158
[8]	K. He, J. Sun and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, 2011, pp. 2341-2353. http://dx.doi.org/10.1109/TPAMI.2010.168
[9]	R. FitzHugh, “Impulses and Physiological States in Theoretical Models of Nerve Membrane,” Biophysical Journal, Vol. 1, No. 6, 1961, pp. 445-466. http://dx.doi.org/10.1016/S0006-3495(61)86902-6
[10]	H. R. Wilson and J. D. Cowan, “Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons,” Biophysical Journal, Vol. 12, No. 1, 1972, pp. 1-24. http://dx.doi.org/10.1016/S0006-3495(72)86068-5
[11]	J. Hindmarsh and R. Rose, “A Model of Neuronal Bursting Using Three Coupled First Order Differential Equations,” Proceedings of the Royal Society of London. Series B. Biological Sciences, Vol. 221, No. 1222, 1984, pp. 87-102. http://dx.doi.org/10.1098/rspb.1984.0024
[12]	J. J. Hopfield, D. W. Tank, et al., “Computing with Neural Circuits: A Model,” Science, Vol. 233, No. 4764, 1986, pp. 625-633. http://dx.doi.org/10.1126/science.3755256
[13]	H. Hu and Z. Shi, “The Possibility of Using Simple Neuron Models to Design Brain-Like Computers,” In: Advances in Brain Inspired Cognitive Systems, Springer, Shenyang, 2012, pp. 361-372. http://dx.doi.org/10.1007/978-3-642-31561-9_41
[14]	H. Abarbanel, M. I. Rabinovich, A. Selverston, M. Bazhenov, R. Huerta, M. Sushchik, and L. Rubchinskii, “Synchronisation in Neural Networks,” Physics-Uspekhi, Vol. 39, No. 4, 1996 pp. 337-362. http://dx.doi.org/10.1070/PU1996v039n04ABEH000141
[15]	Z. Li, “A Neural Model of Contour Integration in the Primary Visual Cortex,” Neural Computation, Vol. 10, No. 4, 1998, pp. 903-940. http://dx.doi.org/10.1162/089976698300017557
[16]	E. Fransen and A. Lansner, “A Model of Cortical Associative Memory Based on a Horizontal Network of Connected Columns,” Network: Computation in Neural Systems, Vol. 9, No. 2, 1998, pp. 235-264. http://dx.doi.org/10.1088/0954-898X/9/2/006
[17]	H. Sun, L. Liu and A. Guo, “A Neurocomputational Model of Figure-Ground Discrimination and Target Tracking,” IEEE Transactions on Neural Networks, Vol. 10, No. 4, 1999, pp. 860-884. http://dx.doi.org/10.1109/72.774238
[18]	H. B. Barlow, C. Blakemore and J. D. Pettigrew, “The Neural Mechanism of Binocular Depth Discrimination,” The Journal of Physiology, Vol. 193, No. 2, 1967, p. 327.
[19]	D. H. Hubel and T. N. Wiesel, “Stereoscopic Vision in Macaque Monkey: Cells Sensitive to Binocular Depth in area 18 of the Macaque Monkey Cortex,” Nature, Vol. 225, 1970, pp. 41-42. http://dx.doi.org/10.1038/225041a0
[20]	K. S. Rockland and J. S. Lund, “Intrinsic Laminar Lattice Connections in Primate Visual Cortex,” Journal of Comparative Neurology, Vol. 216, No. 3, 1983, pp. 303-318. http://dx.doi.org/10.1002/cne.902160307
[21]	C. D. Gilbert and T. N. Wiesel, “Clustered Intrinsic Connections in Cat Visual Cortex,” The Journal of Neuroscience, Vol. 3, No. 5, 1983, pp. 1116-1133.
[22]	R. von der Heydt, H. Zhou, H. S. Friedman, et al., “Representation of Stereoscopic Edges in Monkey Visual Cortex,” Vision Research, Vol. 40, No. 15, 2000, pp. 1955-1967. http://dx.doi.org/10.1016/S0042-6989(00)00044-4
[23]	J. S. Bakin, K. Nakayama and C. D. Gilbert, “Visual Responses in Monkey Areas v1 and v2 to Three-Dimensional Surface Configurations,” The Journal of Neuroscience, Vol. 20, No. 21, 2000, pp. 8188-8198.
[24]	L. A. Zadeh, “Information and Control,” Fuzzy Sets, Vol. 8, No. 3, 1965, pp. 338-353.
[25]	S. S. Haykin, “Neural Networks: A Comprehensive Foundation,” Prentice Hall Englewood Cliffs, 2007.
[26]	J. Nagumo, S. Arimoto and S. Yoshizawa, “An Active Pulse Transmission Line Simulating Nerve Axon,” Proceedings of the IRE, Vol. 50, No. 10, 1962, pp. 2061-2070. http://dx.doi.org/10.1109/JRPROC.1962.288235
[27]	A. L. Hodgkins and A. F. Huxley, “A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve,” American Journal of Physiology, Vol. 117, No. 4, 1952, pp. 500-544.
[28]	V. der Pol B, “The Nonlinear Theory of Electrical Oscillations,” Proceedings of the Institute of Radio Engineers, Vol. 22, No. 9, 1934, pp. 1051-1086.
[29]	J. A. Connor, D. Walter and R. McKowN, “Neural Repetitive Firing: Modifications of the Hodgkin-Huxley Axon Suggested by Experimental Results from Crustacean Axons,” Biophysical Journal, Vol. 18, No. 1, 1977, pp. 81-102. http://dx.doi.org/10.1016/S0006-3495(77)85598-7
[30]	C. Morris and H. Lecar, “Voltage Oscillations in the Barnacle Giant Muscle Fiber,” Biophysical Journal, Vol. 35, No. 1, 1981, pp. 193-213. http://dx.doi.org/10.1016/S0006-3495(81)84782-0
[31]	T. R. Chay, “Chaos in a Three-Variable Model of an Excitable Cell,” Physica D: Nonlinear Phenomena, Vol. 16, No. 2, 1985, pp. 233-242. http://dx.doi.org/10.1016/0167-2789(85)90060-0
[32]	T. R. Chay, “Electrical Bursting and Intracellular Ca2+ Oscillations in Excitable Cell Models,” Biological Cybernetics, Vol. 63, No. 1, 1990, pp. 15-23. http://dx.doi.org/10.1007/BF00202449
[33]	D. Golomb, J. Guckenheimer and S. Gueron, “Reduction of a Channel-Based Model for a Stomatogastric Ganglion Lpneuron,” Biological Cybernetics, Vol. 69, No. 2, 1993, pp. 129-137. http://dx.doi.org/10.1007/BF00226196
[34]	H. R. Wilson and J. D. Cowan, “A Mathematical Theory of the Functional Dynamics of Cortical and Thalamic Nervous Tissue,” Kybernetik, Vol. 13, No. 2, 1973, pp. 55-80. http://dx.doi.org/10.1007/BF00288786
[35]	F. Buchholtz, J. Golowasch, I. R. Epstein and E. Marder, “Mathematical Model of an Identified Stomatogastric Ganglionneuron,” Journal of Neurophysiology, Vol. 67, No. 2, 1992, pp. 332-340.
[36]	A. Burkitt, “A Review of the Integrate-and-Fire Neuron Model: I. Homogeneous Synaptic Input,” Biological Cybernetics, Vol. 95, No. 1, 2006, pp. 1-19. http://dx.doi.org/10.1007/s00422-006-0068-6
[37]	H.-X. Li and C. P. Chen, “The Equivalence between Fuzzy Logic Systems and Feedforward Neural Networks,” IEEE Transactions on Neural Networks, Vol. 11, No. 2, 2000, pp. 356-365. http://dx.doi.org/10.1109/72.839006
[38]	G. Fung and O. L. Mangasarian, “Proximal Support Vector Machine Classifiers,” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2011, pp. 77-86. http://dx.doi.org/10.1145/502512.502527
[39]	D. Wielaard, M. Shelley, D. McLaughlin and R. Shapley, “How Simple Cells Are Made in a Nonlinear Network Model of the Visual Cortex,” The Journal of Neuroscience, Vol. 21, No. 14, 2001, pp. 5203-5211.
[40]	J. Wielaard and P. Sajda, “Simulated Optical Imaging of Orientation Preference in a Model of V1,” Proceedings of First International IEEE EMBS Conference on Neural Engineering, Capri Island, 20-22 March 2003, pp. 499-502.
[41]	Kokkinos, R. Deriche, O. Faugeras and P. Maragos, “Computational Analysis and Learning for a Biologically Motivated Model of Boundary Detection,” Neurocomputing, Vol. 71, No. 10, 2008, pp. 1798-1812. http://dx.doi.org/10.1016/j.neucom.2007.11.031
[42]	V. B. Mountcastle, “The Columnar Organization of the Neocortex,” Brain, Vol. 120, No. 4, 1997, pp. 701-722. http://dx.doi.org/10.1093/brain/120.4.701
[43]	T. Ojala, M. Pietikainen and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Featured Distributions,” Pattern Recognition, Vol. 29, No. 1, 1996, pp. 51-59. http://dx.doi.org/10.1016/0031-3203(95)00067-4