The CNN Universal Machine: An Analogic Array Computer

IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing

Volumn 40, Issue 3, 1993, Pages 163-173

  Chua, Leon O ^b  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER HARDWARE; COMPUTER PROGRAMMING; COMPUTER WORKSTATIONS; INTEGRATED CIRCUITS; LOGIC CIRCUITS; LOGIC DEVICES; NEURAL NETWORKS; SUPERCOMPUTERS;

ANALOG ALGORITHMS; ANALOG ARRAY COMPUTERS; CELLULAR NEURAL NETWORKS; NEUROMORPHIC COMPUTING; UNIVERSAL MACHINES;

ANALOG COMPUTERS;

EID: 0027553986     PISSN: 10577130     EISSN: None     Source Type: Journal    
DOI: 10.1109/82.222815     Document Type: Article

References (46)

1. L. O. Chua and L. Yang, “Cellular neural networks: Theory,” IEEE Trans. Circuits Syst., vol. 35, pp. 1257–1272, Oct. 1988.
2. L. O. Chua and L. Yang, “Cellular neural networks: Applications,” IEEE Trans. Circuits Syst., vol. 35, pp. 1273–1290, Oct. 1988.
3. Proc. IEEE Int. Workshop Cellular Neural Networks and their Applications (CNN-90), 1990.
4. Proc. 2nd IEEE Int. Workshop on Cellular Neural Networks and their Applications, 1992.
5. Proc. Special Session mn Cellular Neural Networks European Conf. Circuit Theory and Design, Sept. 1991.
6. Int. J. Circuit Theory and Applications, Special issue on Cellular Neural Networks, vol. 20, 1992.
7. L. O. Chua and T. Roska, “The CNN paradigm,” in this issue.
8. T. Roska, “Analog events and a dual computing structure using analog and digital circuits and operators,” in Discrete Event Systems: Models and Applications, P. Varaiya and A. B. Kurzhanski, Eds. New York: Springer Verlag, 1988, pp. 225–238.
9. C. W. Wu, T. Roska, and L. O. Chua, “Detecting features by CNN’s in oscillating or chaotic mode,” Tech. Rep., 1992.
10. “CNN Workstation (version 5.1),” users’ documentation, SZTAKI, 1992.
11. A. Csurgay, “Fundamental limits in large-scale circuit modelling,” in Proc. European Conf. Circuit Theory and Design pp. 454–457, VDE-Verlag, Berlin, 1983.
12. C. Mead, Analog VLSI and Neural Systems. Reading, MA: Addison Wesley, 1989.
13. Analog VLSI Implementation of neural Systems, C. Mead and M. Ismail, Eds. Boston: Kluwer, 1989.
14. M. Mahowald and C. Mead, “The silicon retina,” Scientific American, vol. 264, pp. 76–82, 1991
15. V. I. Krinsky, V. N. Biktashev, and I. R. Efimov, “Autowave principles for parallel image processing,” Physica D, vol. 49, pp. 247–253, 1991.
16. C. B. Price, P. Wambacq, and A. Osterlinck, “Image enhancement and analysis with reaction-diffusion paradigm,” Proc. Inst. Elec. Eng., vol. 137, pp. 136–145, 1990, Pt. I.
17. “Intel 80170 XA Electrically Trainable Analog Neural Network,” in Advance Information, Intel Corp., 1990.
18. W. Heiligenberg, “The neural basis of behavior: a neuroethological view,” Ann. Rev. Neurosci., vol. 14, pp. 247–267, 1991.
19. P. Baldi and W. Heiligenberg, “How sensory maps could enhance resolution through ordered arrangements of broadly tuned receivers,” Biol. Cyhem., vol. 59, pp. 313–318, 1988.
20. M. Konishi, “Deciphering the brain's codes,” Neural Computation, vol. 3, pp. 1–18, 1991.
21. M. Konishi et al, “Neurophysiological and anatomical substrates of sound localization in the owl,” in Auditory Function, G. M. Edelman, W. E. Gall, and W. M. Cowan, Eds. New York: Wiley, 1988, pp. 721–745.
22. J. Allmann, F. Miezin, and E. McGuiness, “Stimulus specific responses beyond the classical receptive field: Neurophysiological mechanisms for local-global comparisons in visual neurons,” Ann. Rev. Neurosci., vol. 8, pp. 407–430, 1985.
23. G. Montgomery, “The mind’s eye,” Discover, May 1991.
24. J. Hamori, P. P. T. Pasik, and J. Szentagothai, “Triadic synaptic arrangements and their possible significance in the lateral geniculate nucleus of the monkey,” Brain Research, vol. 80, pp. 379–393, 1974.
25. E. Labos, “Theoretical considerations of local neuron circuits and their triadic synaptic arrangements (TSA) in subcortical nuclei,” J. Neuroscience Research, vol. 3, pp. 1–10, 1977.
26. D. C. V. Essen, C. H. Anderson, and D. J. Felleman, “Information processing in the primate visual system: An integrated systems perspective,” Science, vol. 255, pp. 419-423, Jan. 1992.
27. J. L. Bradshaw and N. C. Nettleton, Human Cerebral Assymmetry. Englewood Cliffs, NJ: Prentice Hall, 1983.
28. E. R. Kandel, J. H. Schwarz, and T. M. Jessel, Principles of Neural Science, third edition. Amsterdam: Elsevier, 1991.
29. A. Czobor, Rembrandt and his Circle. Budapest: Corvina Press, 1969.
30. “Special issue on mind and brain,” Scientific American, Sept. 1992.
31. F. S. Werblin, “Synaptic connections, receptive fields, and pattern of activity in the tiger salamander retina,” Investigative Opthalmology and Visual Science, vol. 32, pp. 459–483, 1991.
32. J. L. Teeters and F. S. Werblin. “Real-time simulation of the retina allowing visualization of each processing stage,” SPIE, vol. 1472, 1991. Image Understanding and the Man-machine Interface III.
33. L. O. Chua and T. Roska, “The CNN universal machine—Part 1: The architecture,” in Proc. IEEE CNNA-92, pp. 1–10, 1992.
34. T. Roska and L. O. Chua, “The CNN universal machine—Part 2: Programmability and applications,” in Proc. IEEE CNNA-92, pp. 181–190, 1992.
35. T. Roska, A. Zarandy, and L. O. Chua, “Color image processing by CNN,” in preparation.
36. T. Roska and L. O. Chua, “Cellular neural networks with nonlinear and delay-type template elements,” in IEEE Proc. Int. Workshop on Cellular Neural Networks and Their Applications, pp. 12–25, 1990 (extended version in Int. J. Circuit Theory and Applications, vol. 20, pp. 469-481 1992).
37. Workshop on Unifying models of Biological Sensory Information Processing via Cellular Neural Networks (CNNs), Aug. 3, 1992, Nonlinear Electronics Laboratory, U. C. Berkeley.
38. J. Buhmann and K. Schulten, “Influence of noise on the function of a “physiological” neural network,” Biol. Cvbern. vol. 56, pp. 313–327, 1987.
39. A. G. Radvanyi, “A dual CNN model of cyclopean perception and its application potentials in artificial stereopsis,” Proc. IEEE CNNA-92, pp. 222–227, 1992.
40. L. O. Chua, T. Roska, P. L. Venetianer, and A. Zarandy, “Some novel capabilities of CNN: Game of life and examples of multipath algorithms,” Report DNS-3-1992, Dual and Neural Computing Systems Laboratory, Hungarian Academy of Sciences, 1992.
41. W. Heiligenberg and T. Roska, “On biological sensory information processing principles relevant to dually computing CNN’s,” Rep. DNS-4-1992, Dual and Neural Computing Systems Laboratory, Hungarian Academy of Sciences, 1992.
42. T. Roska, J. Hámori, E. Lábos, K. Lotz, L. Orzó, J. Takács, P. Vanetianer, Z. Vidnyánszky, and A. Zarándy, “The use of CNN models in the subcortical visual pathway,” Rep. DNS-16-1992, Dual and Neural Computing Systems Laboratory, Hungarian Academy of Sciences, 1992, revised version in this issue.
43. V. Perez-Munuzuri, V. Perez-Villar, and L. O. Chau, “Autowaves for image processing on a two-dimensional CNN array of Chua’s circuits: flat and wrinkled labyrinths,” IEEE Trans. Circuits Syst.—I, vol. 40, pp. 174–181, this issue.
44. L. O. Chua, “Global unfolding of Chua’s circuit,” IEICE Trans. on Fundamentals Electron. Commun., Comp. Sci., vol. E76-A, May 1993.
45. J. M. Cruz and L. O. Chua, “A CMOS IC nonlinear resistor for Chua’s circuit,” IEEE Trans. Circuits Syst.—1, vol. 39, pp. 985–995, Dec. 1992.
46. M. Delgado-Restituto and A. Rodriguez-Vazquez, “A CMOS monolithic Chua’s circuit,” in Chua’s Circuit: A Paradigm for Chaos, R. N. Madan, Ed., Singapore: World Scientific, 1993.