[1] B. B. Traore, B. Kamsu-Foguem, F. Tangara. Deep convolution neural network for image recognition. Ecol. Inform., 48, 257-268(2018).

[2] J. Hirschberg, C. D. Manning. Advances in natural language processing. Science, 349, 261-266(2015).

[3] J. Von Neumann. The Computer and the Brain(1958).

[4] B. J. Shastri, A. N. Tait, T. F. de Lima. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021).

[5] X. Sui, Q. Wu, J. Liu. A review of optical neural networks. IEEE Access, 8, 70773-70783(2020).

[6] J. Liu, Q. Wu, X. Sui. Research progress in optical neural networks: theory, applications and developments. PhotoniX, 2, 5(2021).

[7] S. Xiang, Y. Han, Z. Song. A review: photonics devices, architectures, and algorithms for optical neural computing. J. Semicond., 42, 023105(2021).

[8] X. Guo, J. Xiang, Y. Zhang. Integrated neuromorphic photonics: synapses, neurons, and neural networks. Adv. Photon. Res., 2, 2000212(2021).

[9] A. Mehonic, A. J. Kenyon. Brain-inspired computing needs a master plan. Nature, 604, 255-260(2022).

[10] K. Roy, A. Jaiswal, P. Panda. Towards spike-based machine intelligence with neuromorphic computing. Nature, 575, 607-617(2019).

[11] W. Maass. Networks of spiking neurons: the third generation of neural network models. Neural Netw., 10, 1659-1671(1997).

[12] Z. F. Mainen, T. J. Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268, 1503-1506(1995).

[13] J. Gautrais, S. Thorpe. Rate coding versus temporal order coding: a theoretical approach. Biosystems, 48, 57-65(1998).

[14] S. Thorpe, A. Delorme, R. Van Rullen. Spike-based strategies for rapid processing. Neural Netw., 14, 715-725(2001).

[15] E. M. Izhikevich. Simple model of spiking neurons. IEEE Trans. Neural Netw., 14, 1569-1572(2003).

[16] T. Gollisch, M. Meister. Rapid neural coding in the retina with relative spike latencies. Science, 319, 1108-1111(2008).

[17] E. Painkras, L. A. Plana, J. Garside. SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid-State Circuits, 48, 1943-1953(2013).

[18] B. V. Benjamin, P. Gao, E. McQuinn. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE, 102, 699-716(2014).

[19] P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345, 668-673(2014).

[20] J. Pei, L. Deng, S. Song. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 572, 106-111(2019).

[21] J. W. Goodman, A. R. Dias, L. M. Woody. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. Opt. Lett., 2, 1-3(1978).

[22] P. R. Prucnal, B. J. Shastri, T. F. de Lima. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv. Opt. Photon., 8, 228-299(2016).

[23] C. Mesaritakis, A. Kapsalis, A. Bogris. Artificial neuron based on integrated semiconductor quantum dot mode-locked lasers. Sci. Rep., 6, 39317(2016).

[24] B. Kelleher, C. Bonatto, G. Huyet. Excitability in optically injected semiconductor lasers: contrasting quantum-well-and quantum-dot-based devices. Phys. Rev. E, 83, 026207(2011).

[25] S. Barbay, R. Kuszelewicz, A. M. Yacomotti. Excitability in a semiconductor laser with saturable absorber. Opt. Lett., 36, 4476-4478(2011).

[26] F. Selmi, R. Braive, G. Beaudoin. Spike latency and response properties of an excitable micropillar laser. Phys. Rev. E, 94, 042219(2016).

[27] M. A. Nahmias, B. J. Shastri, A. N. Tait. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J. Sel. Top. Quantum Electron., 19, 1800212(2013).

[28] A. Dolcemascolo, B. Garbin, B. Peyce. Resonator neuron and triggering multipulse excitability in laser with injected signal. Phys. Rev. E, 98, 062211(2018).

[29] A. Hurtado, J. Javaloyes. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems. Appl. Phys. Lett., 107, 241103(2015).

[30] S. Xiang, Y. Zhang, X. Guo. Photonic generation of neuron-like dynamics using VCSELs subject to double polarized optical injection. J. Lightwave Technol., 36, 4227-4234(2018).

[31] J. Robertson, E. Wade, Y. Kopp. Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE J. Sel. Top. Quantum Electron., 26, 7700715(2019).

[32] J. Feldmann, N. Youngblood, C. D. Wright. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 569, 208-214(2019).

[33] A. Romeira, J. Javaloyes, C. N. Ironside. Excitability and optical pulse generation in semiconductor lasers driven by resonant tunneling diode photo-detectors. Opt. Express, 21, 20931-20940(2013).

[34] A. H. Jaafar, R. J. Gray, E. Verrelli. Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems. Nanoscale, 9, 17091-17098(2017).

[35] S. Gao, G. Liu, H. Yang. An oxide Schottky junction artificial optoelectronic synapse. ACS Nano, 13, 2634-2642(2019).

[36] S. Xiang, Y. Zhang, J. Gong. STDP-based unsupervised spike pattern learning in a photonic spiking neural network with VCSELs and VCSOAs. IEEE J. Sel. Top. Quantum Electron., 25, 1700109(2019).

[37] S. Xiang, Z. Ren, Z. Song. Computing primitive of fully VCSEL-based all-optical spiking neural network for supervised learning and pattern classification. IEEE Trans. Neural Netw. Learn. Syst., 32, 2494-2505(2020).

[38] S. Xiang, Z. Ren, Y. Zhang. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA. Opt. Lett., 45, 1104-1107(2020).

[39] Y. Zhang, J. Robertson, S. Xiang. All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes. Photon. Res., 9, B201-B209(2021).

[40] Z. Song, S. Xiang, Z. Ren. Spike sequence learning in a photonic spiking neural network consisting of VCSELs-SA with supervised training. IEEE J. Sel. Top. Quantum Electron., 26, 1700209(2020).

[41] J. Robertson, Y. Zhang, M. Hejda. Image edge detection with a photonic spiking VCSEL-neuron. Opt. Express, 28, 37526-37537(2020).

[42] Y. Zhang, S. Xiang, X. Cao. Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task. Photon. Res., 9, 1055-1061(2021).

[43] S. Gao, S. Y. Xiang, Z. W. Song. Motion detection and direction recognition in a photonic spiking neural network consisting of VCSELs-SA. Opt. Express, 30, 31701-31713(2022).

[44] S. Wang, S. Xiang, G. Han. Photonic associative learning neural network based on VCSELs and STDP. J. Lightwave Technol., 38, 4691-4698(2020).

[45] M. Skontranis, G. Sarantoglou, S. Deligiannidis. Time-multiplexed spiking convolutional neural network based on VCSELs for unsupervised image classification. Appl. Sci., 11, 1383(2021).

[46] M. Wuttig, H. Bhaskaran, T. Taubner. Phase-change materials for non-volatile photonic applications. Nat. Photonics, 11, 465-476(2017).

[47] J. Zheng, A. Khanolkar, P. Xu. GST-on-silicon hybrid nanophotonic integrated circuits: a non-volatile quasi-continuously reprogrammable platform. Opt. Mater. Express, 8, 1551-1561(2018).

[48] A. Tanaka, Y. Shoji, M. Kuwahara. Ultra-small, self-holding, optical gate switch using Ge₂Sb₂Te₅ with a multi-mode Si waveguide. Opt. Express, 20, 10283-10294(2012).

[49] Z. Yu, J. Zheng, P. Xu. Ultracompact electro-optical modulator-based Ge₂Sb₂Te₅ on silicon. IEEE Photon. Technol. Lett., 30, 250-253(2017).

[50] H. Zhang, L. Zhou, J. Xu. Nonvolatile waveguide transmission tuning with electrically-driven ultra-small GST phase-change material. Sci. Bull., 64, 782-789(2019).

[51] N. Farmakidis, N. Youngblood, X. Li. Plasmonic nanogap enhanced phase-change devices with dual electrical-optical functionality. Sci. Adv., 5, eaaw2687(2019).

[52] J. Zheng, S. Zhu, P. Xu. Modeling electrical switching of nonvolatile phase-change integrated nanophotonic structures with graphene heaters. ACS Appl. Mater. Interfaces, 12, 21827-21836(2020).

[53] J. Zheng, Z. Fang, C. Wu. Nonvolatile electrically reconfigurable integrated photonic switch enabled by a silicon PIN diode heater. Adv. Mater., 32, 2001218(2020).

[54] Z. Fang, R. Chen, J. Zheng. Non-volatile reconfigurable silicon photonics based on phase-change materials. IEEE J. Sel. Top. Quantum Electron., 28, 8200317(2021).

[55] H. Zhang, L. Zhou, L. Lu. Miniature multilevel optical memristive switch using phase change material. ACS Photon., 6, 2205-2212(2019).

[56] A. Wu, H. Yu, H. Li. Low-loss integrated photonic switch using subwavelength patterned phase change material. ACS Photon., 6, 87-92(2018).

[57] F. Xiong, A. D. Liao, D. Estrada. Low-power switching of phase-change materials with carbon nanotube electrodes. Science, 332, 568-570(2011).

[58] M. Stegmaier, C. Ríos, H. Bhaskaran. Nonvolatile all-optical 1 × 2 switch for chipscale photonic networks. Adv. Opt. Mater., 5, 1600346(2017).

[59] K. Kato, M. Kuwahara, H. Kawashima. Current-driven phase-change optical gate switch using indium–tin-oxide heater. Appl. Phys. Express, 10, 072201(2017).

[60] I. Chakraborty, G. Saha, A. Sengupta. Toward fast neural computing using all-photonic phase change spiking neurons. Sci. Rep., 8, 12980(2018).

[61] I. Chakraborty, G. Saha, K. Roy. Photonic in-memory computing primitive for spiking neural networks using phase-change materials. Phys. Rev. Appl., 11, 014063(2019).

[62] J. Xiang, A. Torchy, X. Guo. All-optical spiking neuron based on passive microresonator. J. Lightwave Technol., 38, 4019-4029(2020).

[63] J. Xiang, Y. Zhang, Y. Zhao. All-optical silicon microring spiking neuron. Photon. Res., 10, 939-946(2022).

[64] Q. Zhang, N. Jiang, A. Li. All-optical synaptic neuron based on add-drop microring resonator with power-tunable auxiliary light. Opt. Lett., 48, 3167-3170(2023).

[65] Q. Zhang, N. Jiang, G. Hu. All-optical leaky-integrate-and-fire neuron based cascaded microrings with power-tunable auxiliary light. CLEO 2023, JTu2A-55(2023).

[66] H. Zhou, Y. Geng, W. Cui. Soliton bursts and deterministic dissipative Kerr soliton generation in auxiliary-assisted microcavities. Light Sci. Appl., 8, 50610(2019).

[67] Q. Zhang, B. Liu, Q. Wen. Low-noise amplification of dissipative Kerr soliton microcomb lines via optical injection locking lasers. Chin. Opt. Lett., 19, 121401(2021).

[68] W. Bogaerts, P. De Heyn, T. Van Vaerenbergh. Silicon microring resonators. Laser Photon. Rev., 6, 47-73(2012).

[69] I. Bazian. Photonic crystal add–drop filter: a review on principles and applications. Photon. Netw. Commun., 41, 57-77(2021).

[70] Z. Qiang, W. Zhou, R. A. Soref. Optical add-drop filters based on photonic crystal ring resonators. Opt. Express, 15, 1823-1831(2007).

[71] T. Van Vaerenbergh, M. Fiers, P. Mechet. Cascadable excitability in microrings. Opt. Express, 20, 20292-20308(2012).

[72] T. J. Johnson, M. Borselli, O. Painter. Self-induced optical modulation of the transmission through a high-Q silicon microdisk resonator. Opt. Express, 14, 817-831(2006).

[73] T. Uesugi, B.-S. Song, T. Asano. Investigation of optical nonlinearities in an ultra-high-Q Si nanocavity in a two-dimensional photonic crystal slab. Opt. Express, 14, 377-386(2006).

[74] P. E. Barclay, K. Srinivasan, O. Painter. Nonlinear response of silicon photonic crystal microresonators excited via an integrated waveguide and fiber taper. Opt. Express, 13, 801-820(2005).

[75] X. Yang, C. W. Wong. Coupled-mode theory for stimulated Raman scattering in high-Q/V_m, silicon photonic band gap defect cavity lasers. Opt. Express, 15, 4763-4780(2007).

[76] I. Borghi, D. Bazzanella, M. Mancinelli. On the modeling of thermal and free carrier nonlinearities in silicon-on-insulator microring resonators. Opt. Express, 29, 4363-4377(2021).

[77] M. Vollmer. Newton’s law of cooling revisited. Eur. J. Phys., 30, 1063(2009).

[78] M. N. Brunstein, A. M. Yacomotti, I. Sagnes. Excitability and self-pulsing in a photonic crystal nanocavity. Phys. Rev. A, 85, 031803(2012).

[79] A. M. Yacomotti, P. Monnier, F. Raineri. Fast thermo-optical excitability in a two-dimensional photonic crystal. Phys. Rev. Lett., 97, 143904(2006).

[80] T. Tanabe, M. Notomi, S. Mitsugi. Fast bistable all-optical switch and memory on a silicon photonic crystal on-chip. Opt. Lett., 30, 2575-2577(2005).

[81] M. Notomi, A. Shinya, S. Mitsugi. Optical bistable switching action of Si high-Q photonic-crystal nanocavities. Opt. Express, 13, 2678-2687(2005).

[82] N. Cazier, X. Checoury, L.-D. Haret. High-frequency self-induced oscillations in a silicon nanocavity. Opt. Express, 21, 13626-13638(2013).

[83] A. Armaroli, S. Malaguti, G. Bellanca. Oscillatory dynamics in nanocavities with noninstantaneous Kerr response. Phys. Rev. A, 84, 053816(2011).

[84] I. Yang, T. Gu, J. Zheng. Radio frequency regenerative oscillations in monolithic high-Q/V heterostructured photonic crystal cavities. Appl. Phys. Lett., 104, 061104(2014).

[85] M. Wuttig, N. Yamada. Phase-change materials for rewriteable data storage. Nat. Mater., 6, 824-832(2007).

[86] T. Yagi, K. Tamano, Y. Sato. Analysis on thermal properties of tin doped indium oxide films by picosecond thermoreflectance measurement. J. Vac. Sci. Technol. A, 23, 1180-1186(2005).

[87] Z. Ma, Z. Li, K. Liu. Indium-tin-oxide for high-performance electro-optic modulation. Nanophotonics, 4, 198-213(2015).

[88] W. Liu, K. Etessam-Yazdani, R. Hussin. Modeling and data for thermal conductivity of ultrathin single-crystal SOI layers at high temperature. IEEE Trans. Electron Devices, 53, 1868-1876(2006).

[89] W. Fang, Y. Chen, J. Ding. SpikingJelly: an open-source machine learning infrastructure platform for spike-based intelligence. Sci. Adv., 9, eadi1480(2023).

[90] A. Paszke, S. Gross, F. Massa. PyTorch: an imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 7997-8005(2020).