Skip to main content
Log in

Experimental and numerical demonstration of hierarchical time-delay reservoir computing based on cascaded VCSELs with feedback and multiple injections

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In this paper, we propose and demonstrate experimentally and numerically a hierarchical time-delay optical reservoir computing (RC) system based on cascaded vertical-cavity surface-emitting lasers (VCSELs) with feedback and multiple injections. The prediction performance characteristics of the hierarchical time-delay RC system based on cascaded VCSELs under different reservoir layers are compared. Evidently, the prediction performance of the hierarchical time-delay RC system is first improved and saturates as the number of reservoir layers increases. Besides, the impacts of key factors on predicting the hierarchical time-delay RC system are also analyzed in detail experimentally and numerically. This proposed hierarchical time-delay RC system based on VCSELs is useful for the further development of RC systems and may be beneficial to improve the ability of RC systems to solve more complex problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2004, 345: 668–673

    Article  Google Scholar 

  2. Poo M, Du J, Ip N Y, et al. China brain project: basic neuroscience, brain diseases, and brain-inspired computing. Neuron, 2016, 92: 591–596

    Article  Google Scholar 

  3. Wang R, Ren Q S, Zhao J Y. Research progress on photonic neuromorphic computing. Laser Optoelectron Prog, 2016, 53: 120004

    Article  Google Scholar 

  4. Verstraeten D, Schrauwen B, D’Haene M, et al. An experimental unification of reservoir computing methods. Neural Netw, 2007, 20: 391–403

    Article  MATH  Google Scholar 

  5. Hinaut X, Lance F, Droin C, et al. Corticostriatal response selection in sentence production: insights from neural network simulation with reservoir computing. Brain Language, 2015, 150: 54–68

    Article  Google Scholar 

  6. Cucchi M, Gruener C, Petrauskas L, et al. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. Sci Adv, 2021, 7: eabh0693

    Article  Google Scholar 

  7. Jaeger H, Haas H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 2004, 304: 78–80

    Article  Google Scholar 

  8. Maass W. Liquid state machines: motivation, theory, and applications. In: Computability in Context: Computation and Logic in the Real World. Singapore: World Scientific, 2011

    MATH  Google Scholar 

  9. Cuchiero C, Gonon L, Grigoryeva L, et al. Discrete-time signatures and randomness in reservoir computing. IEEE Trans Neural Netw Learn Syst, 2022, 33: 6321–6330

    Article  MathSciNet  Google Scholar 

  10. Zhong Y, Tang J, Li X, et al. Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing. Nat Commun, 2021, 12: 408

    Article  Google Scholar 

  11. Appeltant L, Soriano M C, van der Sande G, et al. Information processing using a single dynamical node as complex system. Nat Commun, 2011, 2: 468–473

    Article  Google Scholar 

  12. Larger L, Soriano M C, Brunner D, et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. Opt Express, 2012, 20: 3241–3249

    Article  Google Scholar 

  13. Guo X X, Xiang S Y, Zhang Y H, et al. Enhanced memory capacity of a neuromorphic reservoir computing system based on a VCSEL with double optical feedbacks. Sci China Inform Sci, 2020, 63: 160407

    Article  MathSciNet  Google Scholar 

  14. Sugano C, Kanno K, Uchida A. Reservoir computing using multiple lasers with feedback on a photonic integrated circuit. IEEE J Sel Top Quantum Electron, 2020, 26: 1–9

    Article  Google Scholar 

  15. Hou Y S, Xia G Q, Yang W Y, et al. Prediction performance of reservoir computing system based on a semiconductor laser subject to double optical feedback and optical injection. Opt Express, 2018, 26: 10211–10219

    Article  Google Scholar 

  16. Huang Y, Zhou P, Yang Y, et al. Time-delayed reservoir computing based on a two-element phased laser array for image identification. IEEE Photon J, 2021, 13: 1–9

    Google Scholar 

  17. Koyama F. Recent advances of VCSEL photonics. J Lightwave Technol, 2006, 24: 4502–4513

    Article  Google Scholar 

  18. Xiang S, Ren Z, Zhang Y, et al. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA. Opt Lett, 2020, 45: 1104–1107

    Article  Google Scholar 

  19. Li N, Susanto H, Cemlyn B R, et al. Stability and bifurcation analysis of spin-polarized vertical-cavity surface-emitting lasers. Phys Rev A, 2017, 96: 013840

    Article  Google Scholar 

  20. Xiang S, Zhang Y, Gong J, et al. STDP-based unsupervised spike pattern learning in a photonic spiking neural network with VCSELs and VCSOAs. IEEE J Sel Top Quantum Electron, 2019, 25: 1–9

    Article  Google Scholar 

  21. Ling W A, Lyubomirsky I, Rodes R, et al. Single-channel 50G and 100G discrete multitone transmission with 25G VCSEL technology. J Lightwave Technol, 2015, 33: 761–767

    Article  Google Scholar 

  22. Vatin J, Rontani D, Sciamanna M. Enhanced performance of a reservoir computer using polarization dynamics in VCSELs. Opt Lett, 2018, 43: 4497–4500

    Article  Google Scholar 

  23. Vatin J, Rontani D, Sciamanna M. Experimental reservoir computing using VCSEL polarization dynamics. Opt Express, 2019, 27: 18579–18584

    Article  Google Scholar 

  24. Guo X X, Xiang S Y, Zhang Y H, et al. Polarization multiplexing reservoir computing based on a VCSEL with polarized optical feedback. IEEE J Sel Top Quantum Electron, 2020, 26: 1–9

    Google Scholar 

  25. Guo X X, Xiang S Y, Zhang Y H, et al. Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system. Opt Express, 2019, 27: 23293–23306

    Article  Google Scholar 

  26. Tao J Y, Wu Z M, Yue D Z, et al. Performance enhancement of a delay-based reservoir computing system by using gradient boosting technology. IEEE Access, 2020, 8: 151990

    Article  Google Scholar 

  27. Nguimdo R M, Lacot E, Jacquin O, et al. Prediction performance of reservoir computing systems based on a diode-pumped erbium-doped microchip laser subject to optical feedback. Opt Lett, 2017, 42: 375–378

    Article  Google Scholar 

  28. Nguimdo R M, Erneux T. Enhanced performances of a photonic reservoir computer based on a single delayed quantum cascade laser. Opt Lett, 2019, 44: 49–52

    Article  Google Scholar 

  29. Jiang N, Pan W, Luo B, et al. Bidirectional dual-channel communication based on polarization-division-multiplexed chaos synchronization in mutually coupled VCSELs. IEEE Photon Technol Lett, 2012, 24: 1094–1096

    Article  Google Scholar 

  30. Deng T, Robertson J, Hurtado A. Controlled propagation of spiking dynamics in vertical-cavity surface-emitting lasers: towards neuromorphic photonic networks. IEEE J Sel Top Quantum Electron, 2017, 23: 1–8

    Google Scholar 

  31. Deng T, Robertson J, Wu Z M, et al. Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks. IEEE Access, 2018, 6: 67951–67958

    Article  Google Scholar 

  32. Jiang N, Zhao A, Xue C, et al. Physical secure optical communication based on private chaotic spectral phase encryption/decryption. Opt Lett, 2019, 44: 1536–1539

    Article  Google Scholar 

  33. Zhang H, Xiang S, Zhang Y, et al. Complexity-enhanced polarization-resolved chaos in a ring network of mutually coupled vertical-cavity surface-emitting lasers with multiple delays. Appl Opt, 2017, 56: 6728–6734

    Article  Google Scholar 

  34. Nguimdo R M, Verschaffelt G, Danckaert J, et al. Simultaneous computation of two independent tasks using reservoir computing based on a single photonic nonlinear node with optical feedback. IEEE Trans Neural Netw Learn Syst, 2015, 26: 3301–3307

    Article  MathSciNet  Google Scholar 

  35. Weigend A S, Gershenfeld N A. Time series prediction: forecasting the future and understanding the past. Intern J Forecast, 1994, 10: 161–163

    Article  Google Scholar 

  36. Tanaka G, Yamane T, Heroux J B, et al. Recent advances in physical reservoir computing: a review. Neural Netw, 2019, 115: 100–123

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant Nos. 2021YFB2801900, 2021YFB2801901, 2021YFB2801902, 2021YFB2801903, 2021YFB2801904), National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (Grant No. 62022062), National Natural Science Foundation of China (Grant Nos. 62204196, 61974177, 61674119), and Key Lab of Modern Optical Technologies of Jiangsu Province, Soochow University (Grant No. KJS2140).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shuiying Xiang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, X., Xiang, S., Cao, X. et al. Experimental and numerical demonstration of hierarchical time-delay reservoir computing based on cascaded VCSELs with feedback and multiple injections. Sci. China Inf. Sci. 67, 122403 (2024). https://doi.org/10.1007/s11432-022-3618-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-022-3618-3

Keywords

Navigation