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Optics Letters


  • Vol. 36, Iss. 1 — Jan. 1, 2011
  • pp: 19–21

Signal feature recognition based on lightwave neuromorphic signal processing

Mable P. Fok, Hannah Deming, Mitchell Nahmias, Nicole Rafidi, David Rosenbluth, Alexander Tait, Yue Tian, and Paul R. Prucnal  »View Author Affiliations

Optics Letters, Vol. 36, Issue 1, pp. 19-21 (2011)

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We developed a hybrid analog/digital lightwave neuromorphic processing device that effectively performs signal feature recognition. The approach, which mimics the neurons in a crayfish responsible for the escape response mechanism, provides a fast and accurate reaction to its inputs. The analog processing portion of the device uses the integration characteristic of an electro-absorption modulator, while the digital processing portion employ optical thresholding in a highly Ge-doped nonlinear loop mirror. The device can be configured to respond to different sets of input patterns by simply varying the weights and delays of the inputs. We experimentally demonstrated the use of the proposed lightwave neuromorphic signal processing device for recognizing specific input patterns.

© 2011 Optical Society of America

OCIS Codes
(070.4340) Fourier optics and signal processing : Nonlinear optical signal processing
(200.4700) Optics in computing : Optical neural systems
(320.7085) Ultrafast optics : Ultrafast information processing

ToC Category:
Fourier Optics and Signal Processing

Original Manuscript: September 1, 2010
Revised Manuscript: November 10, 2010
Manuscript Accepted: November 12, 2010
Published: December 16, 2010

Virtual Issues
Vol. 6, Iss. 2 Virtual Journal for Biomedical Optics

Mable P. Fok, Hannah Deming, Mitchell Nahmias, Nicole Rafidi, David Rosenbluth, Alexander Tait, Yue Tian, and Paul R. Prucnal, "Signal feature recognition based on lightwave neuromorphic signal processing," Opt. Lett. 36, 19-21 (2011)

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