Photonic Neuromorphic Signal Processing and Computing

  • Alexander N. Tait
  • Mitchell A. Nahmias
  • Yue Tian
  • Bhavin J. Shastri
  • Paul R. PrucnalEmail author
Part of the Nano-Optics and Nanophotonics book series (NON)


There has been a recent explosion of interest in spiking neural networks (SNNs), which code information as spikes or events in time. Spike encoding is widely accepted as the information medium underlying the brain, but it has also inspired a new generation of neuromorphic hardware. Although electronics can match biological time scales and exceed them, they eventually reach a bandwidth fan-in trade-off. An alternative platform is photonics, which could process highly interactive information at speeds that electronics could never reach. Correspondingly, processing techniques inspired by biology could compensate for many of the shortcomings that bar digital photonic computing from feasibility, including high defect rates and signal control problems. We summarize properties of photonic spike processing and initial experiments with discrete components. A technique for mapping this paradigm to scalable, integrated laser devices is explored and simulated in small networks. This approach promises to wed the advantageous aspects of both photonic physics and unconventional computing systems. Further development could allow for fully scalable photonic networks that would open up a new domain of ultrafast, robust, and adaptive processing. Applications of this technology ranging from nanosecond response control systems to fast cognitive radio could potentially revitalize specialized photonic computing.


Independent Component Analysis Independent Component Analysis Saturable Absorber Semiconductor Optical Amplifier Excitable Laser 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by Lockheed Martin Advanced Technology Laboratory through the IRAD program, as well as the Lockheed Martin Corporation through the Corporate University Research Program. The authors also acknowledge the support of the NSF MIRTHE Center at Princeton University, the Pyne Fund and Essig Enright Fund for Engineering in Neuroscience. The work of M. A. Nahmias and A. N. Tait was supported by the National Science Foundation Graduate Research Fellowship (NSF-GRF). The work of B. J. Shastri was supported by the National Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship (PDF).


  1. 1.
    R. Sarpeshkar, Neural Comput. 10(7), 1601 (1998)CrossRefGoogle Scholar
  2. 2.
    C. Koch, Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience) (Oxford University Press, 1998)Google Scholar
  3. 3.
    P. Merolla, J. Arthur, F. Akopyan, N. Imam, R. Manohar, D. Modha, in Custom Integrated Circuits Conference (CICC). IEEE 2011, 1–4 (2011)Google Scholar
  4. 4.
    J. Seo, B. Brezzo, Y. Liu, B. Parker, S. Esser, R. Montoye, B. Rajendran, J. Tierno, L. Chang, D. Modha, et al., in Custom Integrated Circuits Conference (CICC), (IEEE, 2011), pp. 1–4Google Scholar
  5. 5.
    K. Likharev, A. Mayr, I. Muckra, Ö. Türel, Ann. N. Y. Acad. Sci. 1006(1), 146 (2003)ADSCrossRefGoogle Scholar
  6. 6.
    G. Snider, Nanotechnology 18(36), 365202 (2007)CrossRefGoogle Scholar
  7. 7.
    Y. Abu-Mostafa, D. Psaltis, Sci. Am. 256(3), 88 (1987)CrossRefGoogle Scholar
  8. 8.
    S. Jutamulia, F. Yu, Opt. Laser Technol. 28(2), 59 (1996)ADSCrossRefGoogle Scholar
  9. 9.
    M. Hill, E. Frietman, H. de Waardt, G. Khoe, H. Dorren, IEEE Trans. Neural Netw. 13(6), 1504 (2002)CrossRefGoogle Scholar
  10. 10.
    S. Thorpe, A. Delorme, R. Van Rullen et al., Neural Netw. 14(6–7), 715 (2001)CrossRefGoogle Scholar
  11. 11.
    W. Maass, Neural Netw. 10(9), 1659 (1997)CrossRefGoogle Scholar
  12. 12.
    D. Tal, E. Schwartz, Neural Comput. 9(2), 305 (1997)CrossRefzbMATHGoogle Scholar
  13. 13.
    B. Lindner, L. Schimansky-Geier, A. Longtin, Phys. Rev. E 66(3), 031916 (2002)Google Scholar
  14. 14.
    Y. Sakai, S. Funahashi, S. Shinomoto et al., Neural Netw. Official J. Int. Neural Netw. Soc. 12(7–8), 1181 (1999)CrossRefGoogle Scholar
  15. 15.
    W. Maass, C.M. Bishop (eds.), Pulsed neural networks (MIT Press, Cambridge, MA, 1999)Google Scholar
  16. 16.
    D. Rosenbluth, K. Kravtsov, M.P. Fok, P.R. Prucnal, Opt. Exp. 17(25), 22767 (2009)ADSCrossRefGoogle Scholar
  17. 17.
    K. Kravtsov, M.P. Fok, D. Rosenbluth, P.R. Prucnal, Opt. Exp. 19(3), 2133 (2011)ADSCrossRefGoogle Scholar
  18. 18.
    M.P. Fok, H. Deming, M. Nahmias, N. Rafidi, D. Rosenbluth, A. Tait, Y. Tian, P.R. Prucnal, Opt. Lett. 36(1), 19 (2011)ADSCrossRefGoogle Scholar
  19. 19.
    Y. Tian, M. Fok, P. Prucnal, in 2011 Conference on IEEE Lasers and Electro-Optics (CLEO), (2011), pp. 1–2Google Scholar
  20. 20.
    K. Kravtsov, P.R. Prucnal, M.M. Bubnov, Opt. Express 15(20), 13114 (2007)ADSCrossRefGoogle Scholar
  21. 21.
    A. Tait, M. Nahmias, M. Fok, P. Prucnal, in 2012 International Conference on Optical MEMS and Nanophotonics (OMN), (2012), pp. 212–213. doi: 10.1109/OMEMS.2012.6318878
  22. 22.
    A.N.Tait et al., The DREAM: an integrated photonic thresholderGoogle Scholar
  23. 23.
    A.N. Tait, B.J. Shastri, M.P. Fok, M.A. Nahmias, P.R. Prucnal, The DREAM: An integrated photonic thresholder (accepted, 2013)Google Scholar
  24. 24.
    C. Koch, H. Li, Vision Chips: Implementing Vision Algorithms With Analog Vlsi Circuits (IEEE Press, New York, 1995)Google Scholar
  25. 25.
    P.R. Prucnal, M.P. Fok, D. Rosenbluth, K. Kravtsov, in ICO International Conference on Information Photonics (IP), (2011)Google Scholar
  26. 26.
    D. Young, Nerve Cells and Animal Behaviour (Cambridge University Press, Cambridge, 1989)Google Scholar
  27. 27.
    N. Edagawa, M. Suzuki, S. Yamamoto, IEICE Trans. Electron. E81-C(8), 1251 (1998)Google Scholar
  28. 28.
    S. Mahon, G. Casassus, C. Mulle, S. Charpier, J. Physiol 550(Pt 3), 947 (2003)CrossRefGoogle Scholar
  29. 29.
    D. Stellwagen, R.C. Malenka, Nature 440(7087), 1054 (2006)ADSCrossRefGoogle Scholar
  30. 30.
    L.F. Abbott, S.B. Nelson, Nature Neurosci. Suppl. 3, 1178 (2000)CrossRefGoogle Scholar
  31. 31.
    C. Savin, P. Joshi, J. Triesch, PLoS Comput Biol 6(4), e1000757 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    B.J. Shastri, M.D. Levine, Mach. Vision Appl. 18(2), 107 (2007)CrossRefGoogle Scholar
  33. 33.
    J. Triesch, in Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations — Volume Part I, ICANN’05 (Springer, Berlin, 2005), pp. 65–70Google Scholar
  34. 34.
    G. Chechik, Neural Comput. 15(7), 1481 (2003)CrossRefzbMATHGoogle Scholar
  35. 35.
    G. Indiveri, E. Chicca, R. Douglas, Trans. Neur. Netw. 17(1), 211 (2006)CrossRefGoogle Scholar
  36. 36.
    G.S. Snider, in Proceedings of the 2008 IEEE International Symposium on Nanoscale Architecturess NANOARCH ’08 (IEEE Computer Society (Washington, DC, 2008), pp. 85–92Google Scholar
  37. 37.
    S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder, W. Lu, Nano Letters 10(4), 1297 (2010)ADSCrossRefGoogle Scholar
  38. 38.
    G.S. Snider, SciDAC Rev. 10, 58 (2008)Google Scholar
  39. 39.
    M.P. Fok, Y. Tian, D. Rosenbluth, P.R. Prucnal, Opt. Lett. 37(16), 3309 (2012)ADSCrossRefGoogle Scholar
  40. 40.
    J.D. Joannopoulos, P.R. Villeneuve, S. Fan, Solid State Commun. 102(2–3), 165 (1997)ADSCrossRefGoogle Scholar
  41. 41.
    E. Ozbay, Science 311(5758), 189 (2006)ADSCrossRefGoogle Scholar
  42. 42.
    G.M. Wojcik, W.A. Kaminski, Neurocomputing 58–60, 245 (2004)CrossRefGoogle Scholar
  43. 43.
    M.A. Nahmias et al., A leaky integrate-and-fire laser neuron for ultrafast cognitive computingGoogle Scholar
  44. 44.
    M.A. Nahmias et al., An evanescent hybrid silicon laser neuronGoogle Scholar
  45. 45.
    B.J. Shastri et al. Exploring excitability in graphene for spike processing networksGoogle Scholar
  46. 46.
    B.J. Shastri et al., Graphene excitable laser for photonic spike processingGoogle Scholar
  47. 47.
    G. Spühler, R. Paschotta, R. Fluck, B. Braun, M. Moser, G. Zhang, E. Gini, U. Keller, JOSA B 16(3), 376 (1999)ADSCrossRefGoogle Scholar
  48. 48.
    H. Wenzel, U. Bandelow, H. Wunsche, J. Rehberg, IEEE J. Quantum Electron 32(1), 69 (1996)ADSCrossRefGoogle Scholar
  49. 49.
    D. Nugent, R. Plumb, M. Fisher, D. Davies, Electron. Lett. 31(1), 43 (1995)CrossRefGoogle Scholar
  50. 50.
    J. Dubbeldam, B. Krauskopf, Opt. commun. 159(4), 325 (1999)ADSCrossRefGoogle Scholar
  51. 51.
    F. Koyama, J. Lightwave Technol. 24(12), 4502 (2006)MathSciNetADSCrossRefGoogle Scholar
  52. 52.
    S. Barbay, R. Kuszelewicz, A.M. Yacomotti, Opt. Lett. 36(23), 4476 (2011)ADSCrossRefGoogle Scholar
  53. 53.
    Y. Li, T. Wang, R. Linke, Appl. Opt. 35(8), 1282 (1996)ADSCrossRefGoogle Scholar
  54. 54.
    D. Taillaert, W. Bogaerts, P. Bienstman, T. Krauss, P. Van Daele, I. Moerman, S. Verstuyft, K. De Mesel, R. Baets, IEEE J. Quantum Electron 38(7), 949 (2002)Google Scholar
  55. 55.
    D. Louderback, G. Pickrell, H. Lin, M. Fish, J. Hindi, P. Guilfoyle, Electron. Lett. 40(17), 1064 (2004)CrossRefGoogle Scholar
  56. 56.
    L. Coldren, S. Corzine, M. Mashanovitch, Diode Lasers and Photonic Integrated Circuits (New york, Wiley Series in Microwave and Optical Engineering (Wiley, 2011)Google Scholar
  57. 57.
    B.J. Shastri, C. Chen, K.D. Choquette, D.V. Plant, IEEE J. Quantum Electron 47(12), 1537 (2011)ADSCrossRefGoogle Scholar
  58. 58.
    G.E. Giudice, D.V. Kuksenkov, H. Temkin, K.L. Lear, Appl. Phys. Lett. 74(7), 899 (1999)ADSCrossRefGoogle Scholar
  59. 59.
    A. Hurtado, K. Schires, I. Henning, M. Adams, Appl. Phys. Lett. 100(10), 103703 (2012)ADSCrossRefGoogle Scholar
  60. 60.
    A. Hurtado, I.D. Henning, M.J. Adams, Opt. Express 18(24), 25170 (2010) doi: 10.1364/OE.18.025170.
  61. 61.
    E. Izhikevich, IEEE Trans. Neural Netw. 15(5), 1063 (2004)Google Scholar
  62. 62.
    L. Gelens, L. Mashal, S. Beri, W. Coomans, G. Van der Sande, J. Danckaert, G. Verschaffelt, arXiv, preprint arXiv:1108.3704 (2011)Google Scholar
  63. 63.
    W. Coomans, L. Gelens, S. Beri, J. Danckaert, G. Van der Sande, Phys. Rev. E 84(3), 036209 (2011)ADSCrossRefGoogle Scholar
  64. 64.
    M. Herrmann, J.A. Hertz, A. Prugel-Bennett, Netw. Comput. Neural Syst. 6(3), 403 (1995)CrossRefzbMATHGoogle Scholar
  65. 65.
    E.M. Izhikevich, Neural Comput. 18(2), 245 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  66. 66.
    M. Abeles, Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge University Press, Cambridge, 1991)CrossRefGoogle Scholar
  67. 67.
    E. Bienenstock, Netw. Comput. Neural Syst. 6(2), 179 (1995)CrossRefzbMATHGoogle Scholar
  68. 68.
    M.P. Fok, Y. Tian, D. Rosenbluth, P.R. Prucnal, Opt. Lett. (2012)Google Scholar
  69. 69.
    K. Boahen, Neuromorphic, Systems Engineering pp. 229–259 (1998)Google Scholar
  70. 70.
    J. Han, P. Jonker, Nanotechnology 14(2), 224 (2003)ADSCrossRefGoogle Scholar
  71. 71.
    N. Mathur, Nature 419(6907), 573 (2002)ADSCrossRefGoogle Scholar
  72. 72.
    G. Tononi, Biol. Bull. 215(3), 216 (2008)CrossRefGoogle Scholar
  73. 73.
    R. Ananthanarayanan, S.K. Esser, H.D. Simon, D.S. Modha, in Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (ACM, New York, NY, USA, 2009), SC ’09, pp. 63:1–63:12Google Scholar
  74. 74.
    D.S. Modha, R. Ananthanarayanan, S.K. Esser, A. Ndirango, A.J. Sherbondy, R. Singh, Commun. ACM 54(8), 62 (2011)Google Scholar
  75. 75.
    L.S. Smith, A. Hamilton, Neuromorphic Systems: Engineering Silicon from Neurobiology, vol. 10 (World Scientific Publishing Company Incorporated, 1998)Google Scholar
  76. 76.
    K. Vandoorne, W. Dierckx, B. Schrauwen, D. Verstraeten, R. Baets, P. Bienstman, J.V. Campenhout, Opt. Exp. 16(15), 11182 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alexander N. Tait
    • 1
  • Mitchell A. Nahmias
    • 1
  • Yue Tian
    • 1
  • Bhavin J. Shastri
    • 1
  • Paul R. Prucnal
    • 1
    Email author
  1. 1.Lightwave Communications Laboratory, Department of Electrical EngineeringPrinceton UniversityPrincetonUSA

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