OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 33, Iss. 8 — Mar. 10, 1994
  • pp: 1477–1484

Reversal-input superposing technique for all-optical neural networks

Yoshio Hayasaki, Ichiro Tohyama, Toyohiko Yatagai, Masahiko Mori, and Satoshi Ishihara  »View Author Affiliations


Applied Optics, Vol. 33, Issue 8, pp. 1477-1484 (1994)
http://dx.doi.org/10.1364/AO.33.001477


View Full Text Article

Enhanced HTML    Acrobat PDF (1357 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

The proposed technique for optical neural networks can perform all the neural operations in a positive range. Bipolar weights of the neurons are represented by unipolar weights with a positive constant. By superposing the reversal inputs to the weighted sums, we can perform subtraction in a neuron by the nonlinear output function with a negative offset constant. This means that the number of processing elements needed in the proposed system is the same as that of neurons in the original neural network model. An experimental neural system is demonstrated for verification of this technique. The Hopfield model is adapted as an example of the neural networks implemented in the experimental neural system.

© 1994 Optical Society of America

History
Original Manuscript: June 1, 1993
Revised Manuscript: September 7, 1993
Published: March 10, 1994

Citation
Yoshio Hayasaki, Ichiro Tohyama, Toyohiko Yatagai, Masahiko Mori, and Satoshi Ishihara, "Reversal-input superposing technique for all-optical neural networks," Appl. Opt. 33, 1477-1484 (1994)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-33-8-1477


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. T. Kohonen, Self-Organization and Associative Memory (Springer-Verlag, New York, 1984), Chap. 3, p.67.
  2. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Parallel Distributed Processing (MIT, Cambridge, Mass., 1986), Chap. 2, p. 45.
  3. M. A. Sivvilotti, M. R. Emeling, C. A. Mead, “VLSI architectures for implementation of neural networks,” in Proceedings of the AIP Conference on Neural Networks for Computing (American Institute of Physics, New York, 1986), pp. 408–413.
  4. H. P. Graf, L. D. Jackel, R. E. Howard, B. Straughn, B. J. S. Denker, W. Hubbard, D. M. Tennat, D. Schwartz, “VLSI implementation of a neural network memory with several hundreds of neurons,” in Proceedings of the AIP Conference on Neural Networks for Computing (American Institute of Physics, New York, 1986), pp. 182–187.
  5. A. D. Fisher, C. L. Giles, J. N. Lee, “Associative processor architectures for optical computing,” J. Opt. Soc. Am. A 1, 1337 (1984).
  6. N. Farhat, D. Psaltis, “New approach to optical information processing based on Hopfield model,” J. Opt. Soc. Am. A 1, 1296 (1984).
  7. K. Wagner, D. Psaltis, “Multilayer optical learning networks,” Appl. Opt. 26, 5061–5076 (1987). [CrossRef] [PubMed]
  8. N. H. Farhat, “Optoelectronic analogs of self-programming neural nets: architecture and methodologies for implementing fast stochastic learning by simulated annealing,” Appl. Opt. 26, 5093–5103 (1987). [CrossRef] [PubMed]
  9. J. Ohta, M. Takahashi, Y. Nitta, S. Tai, K. Mitsunaga, K. Kyuma, “GaAs/AlGaAs optical synaptic interconnection device for neural networks,” Opt. Lett. 14, 844–846 (1989). [CrossRef] [PubMed]
  10. J. Ohta, Y. Nitta, K. Kyuma, “Dynamic optical neurochip using variable-sensitivity photodiodes,” Opt. Lett. 16, 744–746 (1991). [CrossRef] [PubMed]
  11. S. Zhivkova, M. Miteva, “Image subtraction using fixed holograms in photorefractive Bi12TiO20 crystals,” Opt. Lett. 16, 750–751 (1991). [CrossRef] [PubMed]
  12. T. Hara, M. Sugiyama, Y. Suzuki, “A spatial light modulator,” Adv. Electron. Phys. 64B, 637–639 (1985). [CrossRef]
  13. J. Feinleib, D. S. Oliver, “Reusable optical image storage and processing device,” Appl. Opt. 11, 2752–2759 (1972). [CrossRef] [PubMed]
  14. N. Kasama, Y. Hayasaki, T. Yatagai, M. Mori, S. Ishihara, “Experimental demonstration of optical three-layer neural network,” Jpn. J. Appl. Phys. 29, L1565–L1568 (1990). [CrossRef]
  15. M. Kranzdorf, B. J. Bibner, L. Zhang, K. M. Johnson, “Optical connectionist machine with polarization-based bipolar weight values,” Opt. Eng. 28, 844–848 (1989).
  16. I. Shariv, A. A. Friesem, “All-optical neural network with inhibitory neurons,” Opt. Lett. 14, 485–487 (1989). [CrossRef] [PubMed]
  17. W. Kawakami, H. Yoshinaga, K. Kitayama, “Demonstration of an optaical inhibitory neural network,” Opt. Lett. 14, 984–986 (1989). [CrossRef] [PubMed]
  18. A. P. Ittycheriah, J. F. Walkup, T. F. Krile, S. L. Lim, “Outer product processor using polarization encoding,” Appl. Opt. 29, 275–283 (1990). [CrossRef] [PubMed]
  19. I. Shariv, O. Gila, A. A. Friesem, “All-optical bipolar neural network with polarization-modulating neurons,” Opt. Lett. 16, 1692–1694 (1991). [CrossRef] [PubMed]
  20. M. G. Robinson, K. Johnson, “Noise analysis of polarization-based optoelectronic connectionist machines,” Appl. Opt. 31, 263–272 (1992). [CrossRef] [PubMed]
  21. A. L. Mikaelian, B. S. Kiselyov, N. Y. Kulakov, V. A. Shkitin, V. A. Ivanov, “Optical implementation of high-order associative memory,” Int. Natl. Opt. Comp. 1, 89–92 (1990).
  22. F. T. S. Yu, T. Lu, X. Yang, D. A. Gregory, “Optical neural network with pocket-sized liquid-crystal televisions,” Opt. Lett. 15, 863–865 (1990). [CrossRef] [PubMed]
  23. C. H. Wang, B. K. Jenkins, “Subtracting incoherent optical neural model: analysis, experiment, and applications,” Appl. Opt. 29, 2171–2186 (1990). [CrossRef] [PubMed]
  24. M. Ishikawa, N. Mukouzaka, H. Toyoda, Y. Suzuki, “Optical association: a simple model for optical associative memory,” Appl. Opt. 28, 291–301 (1989). [CrossRef] [PubMed]
  25. M. Ishikawa, N. Mukouzaka, H. Toyoda, Y. Suzuki, “Experimental studies on learning capabilities of optical associative memory,” Appl. Opt. 29, 289–295 (1990). [CrossRef] [PubMed]
  26. J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558 (1982). [CrossRef] [PubMed]
  27. M. Takeda, J. W. Goodman, “Neural networks for computation: number representations and programing complexity,” Appl. Opt. 25, 3033–3046 (1986). [CrossRef] [PubMed]
  28. F. Ito, K. Kitayama, “Optical implementation of the Hopfield neural network using multiple fiber nets,” Appl. Opt. 28, 4176–4181 (1989). [CrossRef] [PubMed]
  29. J. S. Jang, S. M. Jung, S. Y. Lee, S. Y. Shin, “Optical implementation of the Hopfield model for two-dimensional associative memory,” Opt. Lett. 13, 248–250 (1988). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited