This paper presents an all optical fiber based implementation of a hybrid analog-digital computational primitive that provides a basis for complex processing on high bandwidth signals. A natural implementation of a hybrid analog/digital photonic processing primitive is achieved through the integration of new nonlinear fiber, and exploitation of the physics of semiconductor device to process signals in unique ways. Specifically, we describe the use of a semiconductor optical amplifier to implement leaky temporal integration of a signal and a highly Ge-doped nonlinear fiber for thresholding. A straightforward correspondence between our computational primitive and leaky-integrate-and-fire neurons permits leveraging of a large body of research characterizing the computational capabilities of these devices and the emerging pulse processing computational paradigm as a means to implement practical signal processing algorithms in hybrid computing platforms. An experimental demonstration of the behavior of the pulse processing primitive is presented.

© 2009 OSA

1. C. Koch, Biophysics of Computation (Oxford University Press, 1999).
2. Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987). [CrossRef]
3. S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996). [CrossRef]
4. M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002). [CrossRef] [PubMed]
5. R. Sarpeshkar, “Analog versus digital: extrapolating from electronics to neurobiology,” Neural Comput. 10(7), 1601–1638 (1998). [CrossRef] [PubMed]
6. W. Maass, and C. M. Bishop, eds., Pulsed Neural Networks (The MIT Press, 1999).
7. M. Premaratne, D. Neˇsi’c, and G. P. Agrawal, “Pulse amplification and gain recovery in semiconductor optical amplifiers: A systematic analytical approach,” J. Lightwave Technol. 26(12), 1653–1660 (2008). [CrossRef]
8. K. Kravtsov, P. R. Prucnal, and M. M. Bubnov, “Simple nonlinear interferometer-based all-optical thresholder and its applications for optical CDMA,” Opt. Express 15(20), 13114–13122 (2007). [CrossRef] [PubMed]
9. E. M. Dianov and V. M. Mashinsky, “Germania-based core optical fibers,” J. Lightwave Technol. 23(11), 3500–3508 (2005). [CrossRef]

IEEE Trans. Neural Netw.

• M. T. Hill, E. E. E. Frietman, H. de Waardt, G. D. Khoe, and H. S. Dorren, “All fiber-optic neural network using coupled SOA based ring lasers,” IEEE Trans. Neural Netw. 13(6), 1504–1513 (2002). [CrossRef] [PubMed]

J. Lightwave Technol.

• M. Premaratne, D. Neˇsi’c, and G. P. Agrawal, “Pulse amplification and gain recovery in semiconductor optical amplifiers: A systematic analytical approach,” J. Lightwave Technol. 26(12), 1653–1660 (2008). [CrossRef]
• E. M. Dianov and V. M. Mashinsky, “Germania-based core optical fibers,” J. Lightwave Technol. 23(11), 3500–3508 (2005). [CrossRef]

Neural Comput.

• R. Sarpeshkar, “Analog versus digital: extrapolating from electronics to neurobiology,” Neural Comput. 10(7), 1601–1638 (1998). [CrossRef] [PubMed]

Opt. Express

• K. Kravtsov, P. R. Prucnal, and M. M. Bubnov, “Simple nonlinear interferometer-based all-optical thresholder and its applications for optical CDMA,” Opt. Express 15(20), 13114–13122 (2007). [CrossRef] [PubMed]

Opt. Laser Technol.

• S. Jutamulia and F. T. S. Yu, “Overview of hybrid optical neural networks,” Opt. Laser Technol. 28(2), 59–72 (1996). [CrossRef]

Sci. Am.

• Y. Abu-Mostafa and D. Psaltis, “Optical neural computers (invited),” Sci. Am. 256, 88–95 (1987). [CrossRef]

Other

• C. Koch, Biophysics of Computation (Oxford University Press, 1999).
• W. Maass, and C. M. Bishop, eds., Pulsed Neural Networks (The MIT Press, 1999).

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