LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).
Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).
Guo, C. et al. Action2motion: Conditioned generation of 3d human motions. In Proc. of the 28th ACM International Conference on Multimedia, 2021–2029 (Association for Computing Machinery, 2020).
Bubeck, S. et al. Sparks of artificial general intelligence: early experiments with GPT-4. Preprint at https://arxiv.org/abs/2303.12712 (2023).
Fei, N. et al. Towards artificial general intelligence via a multimodal foundation model. Nat. Commun. 13, 3094 (2022).
Moore, G. E. Cramming more components onto integrated circuits. Proc. IEEE 86, 82–85 (1998).
Zhang, C. et al. Optimizing FPGA-based accelerator design for deep convolutional neural networks. In Proc. 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 161–170 (2015).
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).
Horowitz, M. 1.1 computing’s energy problem (and what we can do about it). In 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) 10–14 (Association for Computing Machinery, 2014).
Caulfield, H. J. & Dolev, S. Why future supercomputing requires optics. Nat. Photon. 4, 261–263 (2010).
Chen, Z. et al. Deep learning with coherent VCSEL neural networks. Nat. Photon. 17, 723–730 (2023).
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).
Xu, X. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589, 44–51 (2021).
Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606, 501–506 (2022).
Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 52–58 (2021).
Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).
Miscuglio, M. et al. Massively parallel amplitude-only Fourier neural network. Optica 7, 1812–1819 (2020).
Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon 15, 367–373 (2021).
Tait, A. N. et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep. 7, 7430 (2017).
Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021).
Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020).
Xu, S., Wang, J., Yi, S. & Zou, W. High-order tensor flow processing using integrated photonic circuits. Nat. Commun. 13, 7970 (2022).
Wang, T. et al. Image sensing with multilayer nonlinear optical neural networks. Nat. Photon. 17, 408–415 (2023).
Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).
Larger, L. et al. High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification. Phys. Rev. 7, 011015 (2017).
Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013).
Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014).
Huang, C. et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat. Electron. 4, 837–844 (2021).
Yan, T. et al. Nanowatt all-optical 3D perception for mobile robotics. Sci. Adv. 10, eadn2031 (2024).
Fang, L. et al. Engram-driven videography. Engineering 25, 101–109 (2023).
Zuo, Y. et al. All-optical neural network with nonlinear activation functions. Optica 6, 1132–1137 (2019).
Yan, T. et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett. 123, 023901 (2019).
Xia, F. et al. Nonlinear optical encoding enabled by recurrent linear scattering. Nat. Photon. 18, 1067–1075 (2024).
Wanjura, C. C. & Marquardt, F. Fully nonlinear neuromorphic computing with linear wave scattering. Nat. Phys. 20, 1434–1440 (2024).
Tait, A. N. et al. Silicon photonic modulator neuron. Phys. Rev. Appl. 11, 064043 (2019).
Jha, A., Huang, C. & Prucnal, P. R. Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics. Opt. Lett. 45, 4819–4822 (2020).
Yu, W., Zheng, S., Zhao, Z., Wang, B. & Zhang, W. Reconfigurable low-threshold all-optical nonlinear activation functions based on an add-drop silicon microring resonator. IEEE Photonics J. 14, 1–7 (2022).
Bai, B. et al. Microcomb-based integrated photonic processing unit. Nat. Commun. 14, 66 (2023).
Heebner, J. E., Wong, V., Schweinsberg, A., Boyd, R. W. & Jackson, D. J. Optical transmission characteristics of fiber ring resonators. IEEE J. Quantum Electron. 40, 726–730 (2004).
Chen, S., Zhang, L., Fei, Y. & Cao, T. Bistability and self-pulsation phenomena in silicon microring resonators based on nonlinear optical effects. Opt. Express 20, 7454–7468 (2012).
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).
Zhu, W. et al. Human motion generation: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46, 2430–2449 (2023).
Bandyopadhyay, S. et al. Single-chip photonic deep neural network with forward-only training. Nat. Photon. 18, 1335–1343 (2024).
Hua, S. et al. An integrated large-scale photonic accelerator with ultralow latency. Nature 640, 361–367 (2025).
Ahmed, S. R. et al. Universal photonic artificial intelligence acceleration. Nature 640, 368–374 (2025).
Wang, X. et al. The group interaction field for learning and explaining pedestrian anticipation. Engineering 34, 70–82 (2024).
Koch, C. & Segev, I. The role of single neurons in information processing. Nat. Neurosci. 3, 1171–1177 (2000).
Bliss, T. V. & Collingridge, G. L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39 (1993).
Kholodenko, B. N. Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176 (2006).
Hamerly, R., Bandyopadhyay, S. & Englund, D. Accurate self-configuration of rectangular multiport interferometers. Phys. Rev. Appl. 18, 024019 (2022).
Pai, S. et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380, 398–404 (2023).
Clements, W. R., Humphreys, P. C., Metcalf, B. J., Kolthammer, W. S. & Walmsley, I. A. Optimal design for universal multiport interferometers. Optica 3, 1460–1465 (2016).
Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601, 549–555 (2022).
Xue, Z. et al. Fully forward mode training for optical neural networks. Nature 632, 280–286 (2024).
Trabelsi, C. et al. Deep complex networks. Preprint at https://doi.org/10.48550/arXiv.1705.09792 (2017).
Zhang, H. et al. An optical neural chip for implementing complex-valued neural network. Nat. Commun. 12, 457 (2021).
Xiao, H., Rasul, K. & Vollgraf, R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at https://arxiv.org/abs/1708.07747 (2017).
Orchard, G., Jayawant, A., Cohen, G. K. & Thakor, N. Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9, 437 (2015).
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953).
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B. & Hochreiter, S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inf. Process. Syst. 30, 6627–6638 (2017).
Bińkowski, M., Sutherland, D. J., Arbel, M. & Gretton, A. Demystifying MMD GANs. Preprint at https://doi.org/10.48550/arXiv.1801.01401 (2018).
Xu, Z. et al. Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence. Science 384, 202–209 (2024).
Zhao, P. et al. Ultra-broadband optical amplification using nonlinear integrated waveguides. Nature 640, 918–923 (2025).
Dong, B. et al. Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photon. 17, 1080–1088 (2023).
Reck, M., Zeilinger, A., Bernstein, H. J. & Bertani, P. Experimental realization of any discrete unitary operator. Phys. Rev. Lett. 73, 58 (1994).
Yan, T. Code for a complete photonic integrated neuron (PIN). Zenodo https://doi.org/10.5281/zenodo.14975352 (2025).