SCIENCE CHINA Information Sciences, Volume 63 , Issue 6 : 160408(2020) https://doi.org/10.1007/s11432-020-2887-6

Demonstration of a distributed feedback laser diode working as a graded-potential-signaling photonic neuron and its application to neuromorphic information processing

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  • ReceivedJan 25, 2020
  • AcceptedApr 22, 2020
  • PublishedMay 9, 2020



This work was supported by National Key RD Program of China (Grant No. 2019YFB2203700) and National Natural Science Foundation of China (Grant No. 61822508).


[1] Peng H T, Nahmias M A, de Lima T F. Neuromorphic Photonic Integrated Circuits. IEEE J Sel Top Quantum Electron, 2018, 24: 1-15 CrossRef Google Scholar

[2] Prucnal P R, Shastri B J. Neuromorphic Photonics. Boca Raton: CRC Press, 2017. Google Scholar

[3] Rajamani V, Kim H, Chua L. Morris-Lecar model of third-order barnacle muscle fiber is made of volatile memristors. Sci China Inf Sci, 2018, 61: 060426 CrossRef Google Scholar

[4] Yang C, Adhikari S P, Kim H. Excitatory and inhibitory actions of a memristor bridge synapse. Sci China Inf Sci, 2018, 61: 060427 CrossRef Google Scholar

[5] Li Y, Zhou Y, Wang Z. Memcomputing: fusion of memory and computing. Sci China Inf Sci, 2018, 61: 060424 CrossRef Google Scholar

[6] Prucnal P R, Shastri B J, Ferreira de Lima T. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv Opt Photon, 2016, 8: 228-299 CrossRef Google Scholar

[7] Robertson J, Wade E, Hurtado A. Electrically Controlled Neuron-Like Spiking Regimes in Vertical-Cavity Surface-Emitting Lasers at Ultrafast Rates. IEEE J Sel Top Quantum Electron, 2019, 25: 1-7 CrossRef Google Scholar

[8] Jiang P, Chen C, Liu X B. Generation and characterization of spiking and nonspiking oligodendroglial progenitor cells from embryonic stem cells.. Stem Cells, 2013, 31: 2620-2631 CrossRef PubMed Google Scholar

[9] Eyal G, Verhoog M B, Testa-Silva G, et al. Human cortical pyramidal neurons: from spines to spikes via models. Front Cell Neurosci, 2018, 12: 181. Google Scholar

[10] DiCaprio R A. Information transfer rate of nonspiking afferent neurons in the crab.. J NeuroPhysiol, 2004, 92: 302-310 CrossRef PubMed Google Scholar

[11] Li Z, Liu J, Zheng M. Encoding of both analog- and digital-like behavioral outputs by one C. elegans interneuron.. Cell, 2014, 159: 751-765 CrossRef PubMed Google Scholar

[12] Xiang S Y, Zhang Y H, Gong J K, et al. STDP-Based Unsupervised Spike Pattern Learning in a Photonic Spiking Neural Network With VCSELs and VCSOAs. IEEE J Sel Top Quant, 2019, 25: 1700109. Google Scholar

[13] Ren Q, Zhang Y, Wang R. Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation.. Opt Express, 2015, 23: 25247-25258 CrossRef PubMed Google Scholar

[14] Toole R, Tait A N, Ferreira de Lima T. Photonic Implementation of Spike-Timing-Dependent Plasticity and Learning Algorithms of Biological Neural Systems. J Lightwave Technol, 2016, 34: 470-476 CrossRef Google Scholar

[15] Fok M P, Tian Y, Rosenbluth D. Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity.. Opt Lett, 2013, 38: 419-421 CrossRef PubMed Google Scholar

[16] Zhang J, Gao C, Xue M. Research on frequency modulation character of the current driven DFB semiconductor laser. Mod Phys Lett B, 2019, 33: 1850422 CrossRef Google Scholar

[17] Liu Q, Hollopeter G, Jorgensen E M. Graded synaptic transmission at the Caenorhabditis elegans neuromuscular junction.. Proc Natl Acad Sci USA, 2009, 106: 10823-10828 CrossRef PubMed Google Scholar

[18] Selmi F, Braive R, Beaudoin G. Temporal summation in a neuromimetic micropillar laser.. Opt Lett, 2015, 40: 5690-5693 CrossRef PubMed Google Scholar

[19] Zucker R S, Regehr W G. Short-Term Synaptic Plasticity. Annu Rev Physiol, 2002, 64: 355-405 CrossRef Google Scholar

[20] Hu J, Tang H, Tan K C. How the Brain Formulates Memory: A Spatio-Temporal Model Research Frontier. IEEE Comput Intell Mag, 2016, 11: 56-68 CrossRef Google Scholar

[21] Wang W, Pedretti G, Milo V. Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.. Sci Adv, 2018, 4: eaat4752 CrossRef PubMed Google Scholar

[22] Froemke R C, Dan Y. Spike-timing-dependent synaptic modification induced by natural spike trains.. Nature, 2002, 416: 433-438 CrossRef PubMed Google Scholar

[23] He Y, Nie S, Liu R. Spatiotemporal Information Processing Emulated by Multiterminal Neuro-Transistor Networks.. Adv Mater, 2019, 31: 1900903 CrossRef PubMed Google Scholar

[24] Song Z W, Xiang S Y, Ren Z X. Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection.. Opt Express, 2020, 28: 1561-1573 CrossRef PubMed Google Scholar

[25] Ma B W, Chen J P, Zou W W. A DFB-LD-based photonic neuromorphic network for spatiotemporal pattern recognition. In: proceedings of Optical Fiber Communication Conference, San Diego, 2020. M2K.2. Google Scholar

  • Figure 1

    (Color online) (a) The analytical model for the sound azimuth measurement. $S$ and $O$ are the sound source and the center point, respectively. $\theta$ is the sound azimuth. $L$ represents the distance between ears and center point. $R$ is the distance from $S$ to $O$. $\varphi$ and $D$ are intermediate variables. (b) Experimental setup for the graded-potential-signaling-based properties and neuromorphic processing applications with DFB-LDs. AWG: arbitrary waveform generator; DFB-LD: distributed feedback laser diode; VOA: variable optical attenuator; B-PD: balanced photodetector; OSC: oscilloscope.

  • Figure 2

    (Color online) Simulation results of the graded-potential-signaling-based properties and neuromorphic processing applications with DFB-LDs. The properties of the graded-potential-signaling (a), the temporal integration (b), and the pulse facilitation (c). The neuromorphic processing applications of the pattern recognition (d), the STDP implementation (e), and the sound azimuth measurement (f).

  • Figure 3

    (Color online) Experimental results of the graded-potential-signaling-based properties with DFB-LDs. (a) The graded-potential-signaling property. (b) An output pulse compared with the exponentially-decaying fitted curve. (c)–(e) Responses of a DFB-LD to three input pulses with $\tau$ of 6 $\mu$s, 2.5 $\mu$s (for pulse facilitation property), and 1.5 $\mu$s (for temporal integration property), respectively.

  • Figure 4

    (Color online) The response of a DFB-LD to the sequential input pattern (a) and the input pattern in reverse order (b).

  • Figure 5

    (Color online) The experimental results of the STDP implementation by a DFB-LD. The output at (a) $\Delta~t=-4$ $\mu$s and (b) $\Delta~t=4$ $\mu$s. (c) The peak difference and the largest peak dependent on $\Delta~t$ corresponding to the STDP curve. The effects on the STDP curve of varying (d) $w_{\rm~post}$ and (e) $w_{\rm~pre}$.

  • Figure 6

    (Color online) The experimental results of the sound azimuth measurement with DFB-LDs. The output when (a) Rd $=3$ $\mu$s and (b) Rd $=4$ $\mu$s. The $\Delta~S$ dependent on (c) the Rd and (d) the sound azimuth.