Ham, D., Park, H., Hwang, S. & Kim, K. Neuromorphic electronics based on copying and pasting the brain. Nat. Electron. 4, 635–644 (2021).
Google Scholar
Mead, C. Neuromorphic engineering: in memory of Misha Mahowald. Neural Comput. 35, 343–383 (2023).
Indiveri, G. & Liu, S. C. Memory and information processing in neuromorphic systems. Proc. IEEE 103, 1379–1397 (2015).
Google Scholar
Christensen, D. V. et al. 2022 roadmap on neuromorphic computing and engineering. Neuromorph. Comput. Eng. 2, 022501 (2022).
Google Scholar
Indiveri, G. Neuromorphic is dead. Long live neuromorphic. Neuron 113, 3311–3314 (2025).
Google Scholar
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).
Google Scholar
Pei, J. et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature 572, 106–111 (2019).
Google Scholar
Moradi, S., Qiao, N., Stefanini, F. & Indiveri, G. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 12, 106–122 (2017).
Google Scholar
Neckar, A. et al. Braindrop: a mixed-signal neuromorphic architecture with a dynamical systems-based programming model. Proc. IEEE 107, 144–164 (2019).
Google Scholar
Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020). This paper presents a hybrid training method for memristive neuromorphic hardware.
Google Scholar
Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).
Google Scholar
Chakraborty, I., Roy, D. & Roy, K. Technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars. IEEE Trans. Emerg. Top. Comput. Intell. 2, 335–344 (2018).
Google Scholar
Yang, S. et al. Effective surrogate gradient learning with high-order information bottleneck for spike-based machine intelligence. IEEE Trans. Neural Netw. Learn. Syst. 36, 1734–1748 (2025).
Google Scholar
Neftci, E. O., Mostafa, H. & Zenke, F. Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36, 51–63 (2019).
Google Scholar
Cramer, B. et al. Surrogate gradients for analog neuromorphic computing. Proc. Natl Acad. Sci. USA 119, e2109194119 (2022). This paper presents a hybrid training method for the mixed-signal neuromorphic hardware BrainScaleS.
Google Scholar
Stewart, K., Orchard, G., Shrestha, S. B. & Neftci, E. Online few-shot gesture learning on a neuromorphic processor. IEEE J. Emerg. Sel. Top. Circuits Syst. 10, 512–521 (2020). This paper presents a hybrid training method for the digital neuromorphic hardware Loihi.
Google Scholar
Stenning, K. D. et al. Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks. Nat. Commun. 15, 7377 (2024).
Google Scholar
Kudithipudi, D. et al. Design principles for lifelong learning AI accelerators. Nat. Electron. 6, 807–822 (2023). This paper reviews the design of lifelong learning AI accelerators, including neuromorphic hardware.
Google Scholar
Imam, N. & Cleland, T. A. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat. Mach. Intell. 2, 181–191 (2020). This paper proposes an online training method with Loihi.
Google Scholar
Benjamin, B. V. et al. Neurogrid: a mixed-analog–digital multichip system for large-scale neural simulations. Proc. IEEE 102, 699–716 (2014).
Google Scholar
Qiao, N. et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9, 141 (2015).
Google Scholar
Schmitt, S. et al. Neuromorphic hardware in the loop: training a deep spiking network on the brainscales wafer-scale system. In 2017 International Joint Conference on Neural Networks (IJCNN) 2227–2234 (IEEE, 2017).
Pehle, C. et al. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front. Neurosci. 16, 795876 (2022).
Google Scholar
Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The SpiNNaker project. Proc. IEEE 102, 652–665 (2014).
Google Scholar
Mayr, C., Hoeppner, S. & Furber, S. SpiNNaker 2: a 10 million core processor system for brain simulation and machine learning. In Communicating Process Architectures 2017 & 2018 277–280 (IOS Press, 2019).
Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).
Google Scholar
Yao, M. et al. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat. Commun. 15, 4464 (2024).
Google Scholar
Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601, 549–555 (2022).
Google Scholar
Momeni, A., Rahmani, B., Malléjac, M., Del Hougne, P. & Fleury, R. Backpropagation-free training of deep physical neural networks. Science 382, 1297–1303 (2023).
Google Scholar
Demasius, K. U., Kirschen, A. & Parkin, S. Energy-efficient memcapacitor devices for neuromorphic computing. Nat. Electron. 4, 748–756 (2021).
Google Scholar
Tang, J. et al. Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31, e1902761 (2019).
Google Scholar
Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15, 367–373 (2021).
Google Scholar
Chen, Y. T. et al. All-analog photoelectronic chip for high-speed vision tasks. Nature 623, 48–57 (2023).
Google Scholar
Mozafari, M., Kheradpisheh, S. R., Masquelier, T., Nowzari-Dalini, A. & Ganjtabesh, M. First-spike-based visual categorization using reward-modulated STDP. IEEE Trans. Neural Netw. Learn. Syst. 29, 6178–6190 (2018).
Google Scholar
Wu, Y. et al. Brain-inspired global-local learning incorporated with neuromorphic computing. Nat. Commun. 13, 65 (2022).
Google Scholar
Wang, Y., Liu, H., Zhang, M., Luo, X. & Qu, H. A universal ANN-to-SNN framework for achieving high accuracy and low latency deep spiking neural networks. Neural Netw. 174, 106244 (2024).
Google Scholar
Apolinario, M. P. E., Kosta, A. K., Saxena, U. & Roy, K. Hardware/software co-design with ADC-less in-memory computing hardware for spiking neural networks. IEEE Trans. Emerg. Top. Comput. 12, 35–47 (2023).
Google Scholar
Schuman, C. D., Mitchell, J. P., Patton, R. M., Potok, T. E. & Plank, J. S. Evolutionary optimization for neuromorphic systems. In Proc. 2020 Annual Neuro-Inspired Computational Elements Workshop 1–9 (ACM, 2020).
Yang, S., Linares-Barranco, B., Wu, Y. & Chen, B. Self-supervised high-order information bottleneck learning of spiking neural network for robust event-based optical flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. 47, 2280–2297 (2024). This paper presents a self-supervised information bottleneck training approach for spiking neural networks, enabling improved robustness in offline training of neuromorphic hardware for embodied applications.
Google Scholar
Cheng, Y. C. et al. A comprehensive multimodal benchmark of neuromorphic training frameworks for spiking neural networks. Eng. Appl. Artif. Intell. 159, 111543 (2025).
Google Scholar
Davison, A. P. et al. PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2, 11 (2009).
Bekolay, T. et al. Nengo: a Python tool for building large-scale functional brain models. Front. Neuroinform. 7, 48 (2014).
Google Scholar
Yavuz, E., Turner, J. & Nowotny, T. GeNN: a code generation framework for accelerated brain simulations. Sci. Rep. 6, 18854 (2016).
Google Scholar
Vitay, J., Dinkelbach, H. Ü & Hamker, F. H. ANNarchy: a code generation approach to neural simulations on parallel hardware. Front. Neuroinform. 9, 19 (2015).
Google Scholar
Azghadi, M. R., Iannella, N., Al-Sarawi, S., Indiveri, G. & Abbott, D. Spike based synaptic plasticity in silicon: design, implementation, application, and challenges. Proc. IEEE 102, 717–737 (2014).
Google Scholar
Schemmel, J. et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In 2010 IEEE International Symposium on Circuits and Systems (ISCAS) 1947–1950 (IEEE, 2010).
Yang, S. et al. BiCoSS: toward large-scale cognition brain with multigranular neuromorphic architecture. IEEE Trans. Neural Netw. Learn. Syst. 33, 2801–2815 (2021).
Google Scholar
Frenkel, C., Lefebvre, M., Legat, J.-D. & Bol, D. A 0.086-mm2 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS. IEEE Trans. Biomed. Circuits Syst. 13, 145–158 (2018).
Frenkel, C., Legat, J.-D. & Bol, D. MorphIC: a 65-nm 738k-synapse/mm2 quad-core binary-weight digital neuromorphic processor with stochastic spike-driven online learning. IEEE Trans. Biomed. Circuits Syst. 13, 999–1010 (2019).
Google Scholar
Yang, S. et al. Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans. Neural Netw. Learn. Syst. 33, 7126–7140 (2022).
Google Scholar
Tang, H., Kim, H., Kim, H. & Park, J. Spike counts based low complexity SNN architecture with binary synapse. IEEE Trans. Biomed. Circuits Syst. 13, 1664–1677 (2019).
Google Scholar
Yousefzadeh, A., Stromatias, E., Soto, M., Serrano-Gotarredona, T. & Linares-Barranco, B. On practical issues for stochastic STDP hardware with 1-bit synaptic weights. Front. Neurosci. 12, 665 (2018).
Google Scholar
Camuñas-Mesa, L. A., Vianello, E., Reita, C., Serrano-Gotarredona, T. & Linares-Barranco, B. A CMOL-like memristor-CMOS neuromorphic chip-core demonstrating stochastic binary STDP. IEEE J. Emerg. Sel. Top. Circuits Syst. 12, 898–912 (2022).
Google Scholar
Liang, L. et al. H2learn: high-efficiency learning accelerator for high-accuracy spiking neural networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41, 4782–4796 (2021).
Google Scholar
Tan, P. Y. & Wu, C. W. A 40-nm 1.89-pJ/SOP scalable convolutional spiking neural network learning core with on-chip spatiotemporal back-propagation. IEEE Trans. Very Large Scale Integr. Syst. 31, 1994–2007 (2023).
Google Scholar
Wang, T. et al. MorphBungee: a 65-nm 7.2-mm2 27-μJ/image digital edge neuromorphic chip with on-chip 802-frame/s multi-layer spiking neural network learning. IEEE Trans. Biomed. Circuits Syst. 19, 209–225 (2024).
Google Scholar
Zenke, F. & Neftci, E. O. Brain-inspired learning on neuromorphic substrates. Proc. IEEE 109, 935–950 (2021).
Google Scholar
Rostami, A., Vogginger, B., Yan, Y. & Mayr, C. G. E-prop on SpiNNaker 2: exploring online learning in spiking RNNs on neuromorphic hardware. Front. Neurosci. 16, 1018006 (2022).
Google Scholar
Chen, F. et al. EPOC: a 28-nm 5.3 pJ/SOP event-driven parallel neuromorphic hardware with neuromodulation-based online learning. IEEE Trans. Biomed. Circuits Syst. 19, 629–644 (2024).
Google Scholar
Park, J., Lee, J. & Jeon, D. A 65-nm neuromorphic image classification processor with energy-efficient training through direct spike-only feedback. IEEE J. Solid State Circuits. 55, 108–119 (2019).
Google Scholar
Zhang, J. et al. ANP-I: a 28-nm 1.5-pJ/SOP asynchronous spiking neural network processor enabling sub-0.1-μJ/sample on-chip learning for edge-AI applications. IEEE J. Solid State Circuits. 59, 2717–2729 (2024).
Google Scholar
Frenkel, C., Legat, J. D. & Bol, D. A 28-nm convolutional neuromorphic processor enabling online learning with spike-based retinas. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE, 2020).
Stromatias, E. et al. Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on spinnaker. In 2015 International Joint Conference on Neural Networks (IJCNN) 1–8 (IEEE, 2015).
Ansari, M., Fayyazi, A., Kamal, M., Afzali-Kusha, A. & Pedram, M. OCTAN: an on-chip training algorithm for memristive neuromorphic circuits. IEEE Trans. Circuits Syst. I 66, 4687–4698 (2019).
Azghadi, M. R. et al. Hardware implementation of deep network accelerators towards healthcare and biomedical applications. IEEE Trans. Biomed. Circuits Syst. 14, 1138–1159 (2020).
Google Scholar
D’Agostino, S. et al. DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays. Nat. Commun. 15, 3446 (2024).
Google Scholar
Zhou, P., Choi, D. U., Lu, W. D., Kang, S. M. & Eshraghian, J. K. Gradient-based neuromorphic learning on dynamical RRAM arrays. IEEE J. Emerg. Sel. Top. Circuits Syst. 12, 888–897 (2022).
Google Scholar
Zhang, W. et al. Edge learning using a fully integrated neuro-inspired memristor chip. Science 381, 1205–1211 (2023).
Google Scholar
Shin, U. et al. Pattern training, inference, and regeneration demonstration using on-chip trainable neuromorphic chips for spiking restricted Boltzmann machine. Adv. Intell. Syst. 4, 2200034 (2022).
Google Scholar
Vincent, A. F. et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Trans. Biomed. Circuits Syst. 9, 166–174 (2015).
Google Scholar
Deng, S. et al. Selective area doping for Mott neuromorphic electronics. Sci. Adv. 9, eade4838 (2023).
Google Scholar
Zhu, R. et al. Online dynamical learning and sequence memory with neuromorphic nanowire networks. Nat. Commun. 14, 6697 (2023).
Google Scholar
Bueno, J. et al. Reinforcement learning in a large-scale photonic recurrent neural network. Optica 5, 756–760 (2018).
Google Scholar
van Doremaele, E. R. W., Ji, X., Rivnay, J. & van de Burgt, Y. A retrainable neuromorphic biosensor for on-chip learning and classification. Nat. Electron. 6, 765–770 (2023).
Google Scholar
Miller, J. F., Harding, S. L. & Tufte, G. Evolution-in-materio: evolving computation in materials. Evol. Intell. 7, 49–67 (2014).
Google Scholar
Ortner, T. et al. Rapid learning with phase-change memory-based in-memory computing through learning-to-learn. Nat. Commun. 16, 1243 (2025).
Google Scholar
Kudithipudi, D. et al. Neuromorphic computing at scale. Nature 637, 801–812 (2025).
Google Scholar
Yik, J. et al. The NeuroBench framework for benchmarking neuromorphic computing algorithms and systems. Nat. Commun. 16, 1545 (2025).
Google Scholar
Ostrau, C., Klarhorst, C., Thies, M. & Rückert, U. Benchmarking neuromorphic hardware and its energy expenditure. Front. Neurosci. 16, 873935 (2022).
Google Scholar
Jain, S. & Raghunathan, A. CxDNN: hardware–software compensation methods for deep neural networks on resistive crossbar systems. ACM Trans. Embedded Comput. Syst. 18, 1–23 (2019).
Google Scholar
Liu, C., Hu, M., Strachan, J. P. & Li, H. Rescuing memristor-based neuromorphic design with high defects. In 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC) 1–6 (IEEE, 2017).
Xia, L. et al. Stuck-at fault tolerance in RRAM computing systems. IEEE J. Emerg. Sel. Top. Circuits Syst. 8, 102–115 (2018).
Google Scholar
Liu, T. et al. A fault-tolerant neural network architecture. In Proc. 56th Annual Design Automation Conference 2019 1–6 (ACM, 2019); https://doi.org/10.1145/3316781.3317742
Yuan, G. et al. Improving DNN fault tolerance using weight pruning and differential crossbar mapping for ReRAM-based edge AI. In 2021 22nd International Symposium on Quality Electronic Design (ISQED) 135–141 (2021).
Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019).
Google Scholar
Schuman, C. D. et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2, 10–19 (2022).
Google Scholar
Zhu, D. et al. Neuromorphic visual odometry system for intelligent vehicle application with bio-inspired vision sensor. In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2225–2232 (IEEE, 2019).
Rast, A. D. et al. Behavioral learning in a cognitive neuromorphic robot: an integrative approach. IEEE Trans. Neural Netw. Learn. Syst. 29, 6132–6144 (2018).
Google Scholar
Davies, M. et al. Advancing neuromorphic computing with Loihi: a survey of results and outlook. Proc. IEEE 109, 911–934 (2021). This paper reviews the results that have been obtained so far with Loihi across the major algorithmic domains.
Google Scholar
Wang, S. et al. Memristor-based adaptive neuromorphic perception in unstructured environments. Nat. Commun. 15, 4671 (2024).
Google Scholar
Liu, F. et al. Neuro-inspired electronic skin for robots. Sci. Robot. 7, eabl7344 (2022).
Google Scholar
Bartolozzi, C., Indiveri, G. & Donati, E. Embodied neuromorphic intelligence. Nat. Commun. 13, 1024 (2022).
Google Scholar
Acharya, J. et al. Dendritic computing: branching deeper into machine learning. Neuroscience 489, 275–289 (2022).
Google Scholar
Gordleeva, S. et al. Situation-based neuromorphic memory in spiking neuron-astrocyte network. IEEE Trans. Neural Netw. Learn. Syst. 36, 881–895 (2023).
Google Scholar
Shine, J. M. et al. Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics. Nat. Neurosci. 24, 765–776 (2021).
Google Scholar
Li, Y., Lei, Y. & Yang, X. Spikeformer: training high-performance spiking neural network with transformer. Neurocomputing 574, 127279 (2024).
Google Scholar
Zhu, R.-J., Zhao, Q. Li, G. & Eshraghian, J. K. SpikeGPT: generative pre-trained language model with spiking neural networks. Trans. Mach. Learn. Res. https://openreview.net/forum?id=gcf1anBL9e (2024).
Han, L. et al. CoMN: algorithm-hardware co-design platform for nonvolatile memory-based convolutional neural network accelerators. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43, 2043–2056 (2024).
Google Scholar
Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).
Google Scholar
Wu, X. et al. Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning. Nat. Commun. 14, 468 (2023).
Google Scholar
Chen, S. et al. Electrochemical ohmic memristors for continual learning. Nat. Commun. 16, 2348 (2025).
Google Scholar
Tan, H., Zhou, Y., Tao, Q., Rosen, J. & Dijken, S. Bioinspired multisensory neural network with crossmodal integration and recognition. Nat. Commun. 12, 1120 (2021).
Google Scholar
Eshraghian, J. K. Training spiking neural networks using lessons from deep learning. Proc. IEEE 111, 1016–1054 (2023).
Google Scholar
Fang, W. et al. SpikingJelly: an open-source machine learning infrastructure platform for spike-based intelligence. Sci. Adv 9, eadi1480 (2023).
Google Scholar
Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nat. Mach. Intell. 1, 133–143 (2019).
Google Scholar
Zhao, H. et al. Neural evolutionary architecture search for compact printed analog neuromorphic circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 44, 2655–2668 (2024).
Google Scholar
Yu, F. et al. Brain-inspired multimodal hybrid neural network for robot place recognition. Sci. Robot. 8, eabm6996 (2023).
Google Scholar
Ding, J., Bu, T., Yu, Z., Huang, T. & Liu, J. K. SNN-RAT: robustness-enhanced spiking neural network through regularized adversarial training. Adv. Neural Inf. Process. Syst. 35, 24780–24793 (2022).
Google Scholar
Yang, H. et al. Lead federated neuromorphic learning for wireless edge artificial intelligence. Nat. Commun. 13, 4269 (2022).
Google Scholar
Topolnik, L. & Tamboli, S. The role of inhibitory circuits in hippocampal memory processing. Nat. Rev. Neurosci. 23, 476–492 (2022).
Google Scholar
Faust, T. E., Gunner, G. & Schafer, D. P. Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS. Nat. Rev. Neurosci. 22, 657–673 (2021).
Google Scholar
Chen, L., Li, X., Tjia, M. & Thapliyal, S. Homeostatic plasticity and excitation-inhibition balance: the good, the bad, and the ugly. Curr. Opin. Neurobiol. 75, 102553 (2022).
Google Scholar
Le Gallo, M. et al. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nat. Electron. 6, 680–693 (2023).
Google Scholar
Tong, L. et al. Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2. Nat. Commun. 15, 10290 (2024).
Google Scholar
Donati, E. & Indiveri, G. Neuromorphic bioelectronic medicine for nervous system interfaces: from neural computational primitives to medical applications. Prog. Biomed. Eng. 5, 013002 (2023).
Google Scholar
Painkras, E. et al. SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid State Circuits. 48, 1943–1953 (2013).
Google Scholar
Matsuo, R., Elgaradiny, A. & Corradi, F. Unsupervised classification of spike patterns with the Loihi neuromorphic processor. Electronics 13, 3203 (2024).
Google Scholar
Pedersen, J. E. et al. Neuromorphic intermediate representation: a unified instruction set for interoperable brain-inspired computing. Nat. Commun. 15, 8122 (2024).
Google Scholar
Hu, Y., Zheng, Q., Jiang, X. & Pan, G. Fast-SNN: fast spiking neural network by converting quantized ANN. IEEE Trans. Pattern Anal. Mach. Intell. 45, 14546–14562 (2023).
Google Scholar
Ganguly, C. et al. Spike frequency adaptation: bridging neural models and neuromorphic applications. Commun. Eng. 3, 22 (2024).
Google Scholar
Kudithipudi, D. et al. Biological underpinnings for lifelong learning machines. Nat. Mach. Intell. 4, 196–210 (2022).
Google Scholar
Ochs, M., Dietl, M. & Brederlow, R. A Circuit concept for energy-efficient spiking neural network systems with a FOM of 86.9 fJ/SOP. In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS) 1–5 (IEEE 2024).
Chen, G. K., Kumar, R., Sumbul, H. E., Knag, P. C. & Krishnamurthy, R. K. A 4096-neuron 1M-synapse 3.8pJ/SOP spiking neural network with on-chip STDP learning and sparse weights in 10nm FinFET CMOS. IEEE J. Solid State Circuits. 54, 992–1002 (2018).
Google Scholar
Zhang, J., Liang, M., Wei, J., Wei, S. & Chen, H. A 28nm configurable asynchronous SNN accelerator with energy-efficient learning. In 2021 27th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC) 34–39 (IEEE 2021).
Duan, S., Hu, X., Dong, Z., Wang, L. & Mazumder, P. Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans. Neural Netw. Learn. Syst. 26, 1202–1213 (2014).
Google Scholar
Kim, J. K., Knag, P., Chen, T. & Zhang, Z. A 640M pixel/s 3.65 mW sparse event-driven neuromorphic object recognition processor with on-chip learning. In 2015 Symposium on VLSI Circuits C50–C51 (IEEE, 2015).
Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal–oxide memristors. Nature 521, 61–64 (2015).
Google Scholar
Ambrogio, S. et al. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front. Neurosci. 10, 56 (2016).
Google Scholar
Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018).
Google Scholar
Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).
Google Scholar
Mochida, R. et al. A 4M synapses integrated analog ReRAM based 66.5 TOPS/W neural-network processor with cell current controlled writing and flexible network architecture. In 2018 IEEE Symposium on VLSI Technology 175–176 (IEEE 2018).
Cai, F. A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations. Nat. Electron. 2, 290–299 (2019).
Google Scholar
Cho, S. G., Beigné, E. & Zhang, Z. A 2048-neuron spiking neural network accelerator with neuro-inspired pruning and asynchronous network on chip in 40nm CMOS. In 2019 IEEE Custom Integrated Circuits Conference (CICC) 1–4 (IEEE 2019).
Wang, D. et al. Always-on, sub-300-nW, event-driven spiking neural network based on spike-driven clock-generation and clock-and power-gating for an ultra-low-power intelligent device. In 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC) 1–4 (IEEE 2020).
Nambiar, V. P. et al. 0.5 V 4.8 pJ/SOP 0.93 μW leakage/core neuromorphic processor with asynchronous NoC and reconfigurable LIF neuron. In 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC) 1–4 (IEEE 2020).
Yin, S., Sun, X., Yu, S. & Seo, J. S. High-throughput in-memory computing for binary deep neural networks with monolithically integrated RRAM and 90 nm CMOS. IEEE Trans. Electron Devices 67, 4185–4192 (2020).
Google Scholar
Wong, M. M. et al. A 2.1 pJ/SOP 40nm SNN accelerator featuring on-chip transfer learning using Delta STDP. In ESSDERC 2021-IEEE 51st European Solid-State Device Research Conference (ESSDERC) 95–98 (IEEE, 2021).
Kiani, F., Yin, J., Wang, Z., Joshua Yang, J. & Xia, Q. A fully hardware-based memristive multilayer neural network. Sci. Adv 7, eabj4801 (2021).
Google Scholar
Zhong, Y. et al. A spike-event-based neuromorphic processor with enhanced on-chip STDP learning in 28nm CMOS. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE, 2021).
Höppner, S. et al. The SpiNNaker 2 processing element architecture for hybrid digital neuromorphic computing. Preprint at https://arxiv.org/abs/2103.08392 (2021).
Kuang, Y. et al. A 28-nm 0.34 pJ/SOP spike-based neuromorphic processor for efficient artificial neural network implementations. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE, 2021).
Pu, J., Goh, W. L., Nambiar, V. P., Wong, M. M. & Do, A. T. A 5.28 mm2 4.5 pJ/SOP energy-efficient spiking neural network hardware with reconfigurable high-processing-speed neuron core and congestion-aware router. IEEE. Trans. Circuits Syst. I 68, 5081–5094 (2021).
Stuijt, J., Sifalakis, M., Yousefzadeh, A. & Corradi, F. μBrain: an event-driven and fully synthesizable architecture for spiking neural networks. Front. Neurosci. 15, 664208 (2021).
Google Scholar
Senapati, M., Gomony, M. D., Eissa, S., Frenkel, C. & Corporaal, H. THOR—a neuromorphic processor with 7.29 G TSOP2/mm2 Js energy-throughput efficiency. Preprint at https://arxiv.org/abs/2212.01696 (2022).
Frenkel, C. & Indiveri, G. ReckOn: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales. In 2022 IEEE International Solid-State Circuits Conference (ISSCC) Vol. 65 1–3 (IEEE, 2022).
Choi, S., Lew, D. & Park, J. Early termination based training acceleration for an energy-efficient SNN processor design. IEEE Trans. Biomed. Circuits Syst 16, 442–455 (2022).
Google Scholar
Liu, C. et al. A low-power hybrid-precision neuromorphic processor with INT8 inference and INT16 online learning in 40-nm CMOS. IEEE Trans. Circuits Syst. I 70, 4028–4039 (2023).
Fang, C. et al. A 510μW 0.738-mm2 6.2-pJ/SOP online learning multi-topology SNN processor with unified computation engine in 40-nm CMOS. IEEE Trans. Biomed. Circuits Syst. 17, 507–520 (2023).
Google Scholar
Wang, B. et al. 1.7 pJ/SOP neuromorphic processor with integrated partial sum routers for in-network computing. In 2023 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE 2023).
Tian, F. et al. BIOS: a 40nm bionic sensor-defined 0.47 pJ/SOP, 268.7 TSOPs/W configurable spiking neuron-in-memory processor for wearable healthcare. In 2023 IEEE 49th European Solid State Circuits Conference (ESSCIRC) 225-–228 (IEEE 2023).
Liu, Y. et al. A 22nm 0.43 PJ/SOP sparsity-aware in-memory neuromorphic computing system with hybrid spiking and artificial neural network and configurable topology. In 2023 IEEE Custom Integrated Circuits Conference (CICC) 1–2 (IEEE 2023).
Kim, S. et al. SNPU: an energy-efficient spike domain deep-neural-network processor with two-step spike encoding and shift-and-accumulation unit. IEEE J. Solid State Circuits 58, 2812–2825 (2023).
Google Scholar
Fang, C., Tian, F., Yang, J. & Sawan, M. Accelerating BPTT-based SNN training with sparsity-aware and pipelined architecture. In 2024 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE 2024).
Yang, S. et al. NADOL: neuromorphic architecture for spike-driven online learning by dendrites. IEEE Trans. Biomed. Circuits Syst. 18, 186–199 (2024).
Google Scholar
Zhong, Y. et al. PAICORE: a 1.9-million-neuron 5.181-TSOPS/W digital neuromorphic processor with unified SNN–ANN and on-chip learning paradigm. IEEE J Solid State Circuits 60, 651–671 (2024).
Google Scholar
Ma, D. et al. Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning. Natl Sci. Rev 11, nwae102 (2024).
Google Scholar
Cheng, J. et al. Multimodal deep learning using on-chip diffractive optics with in situ training capability. Nat. Commun. 15, 6189 (2024).
Google Scholar
Zhang, J. et al. ANVMP: a 28nm 52.6 μW 1.25 pJ/SOP asynchronous non-volatile-memory-based computing-in-memory neuromorphic processor for edge-Al applications. In 2024 IEEE Asian Solid-State Circuits Conference (A-SSCC) 1–3 (IEEE 2024).
Lin, Y. H. & Tang, K. T. A 0.2-pJ/SOP digital spiking neuromorphic processor with temporal parallel dataflow and efficient synapse memory compression. In 2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS) 317–321 (IEEE 2024).
Sadeghi, M. et al. NEXUS: a 28nm 3.3 pJ/SOP 16-core spiking neural network with a diamond topology for real-time data processing. IEEE Trans. Biomed. Circuits Syst. https://doi.org/10.1109/TBCAS.2024.3452635 (2024).
Huo, D. et al. ANP-G: a 28-nm 1.04-pJ/SOP sub-mm2 asynchronous hybrid neural network olfactory processor enabling few-shot class-incremental on-chip learning. IEEE J. Solid State Circuits. https://doi.org/10.1109/JSSC.2025.3530513 (2025).
Buhler, F. N. et al. A 3.43 TOPS/W 48.9 pJ/pixel 50.1 nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS. In 2017 Symposium on VLSI CircuitsC30–C31 (IEEE, 2017).
Wilson, M., Bhalla, U., Uhley, J. & Bower, J. GENESIS: a system for simulating neural networks. Adv. Neural Inf. Process. Syst. 1, 485–492 (1988).
Carnevale, N. T. & Hines, M. L. The NEURON Book (Cambridge Univ. Press, 2006); https://doi.org/10.1017/CBO9780511541612
Gewaltig, M.-O. & Diesmann, M. NEST (neural simulation tool). Scholarpedia 2, 1430 (2007).
Google Scholar
Goodman, D. F. & Brette, R. Brian: a simulator for spiking neural networks in Python. Front. Neuroinf. 2, 5 (2008).
Google Scholar
Sanz-Leon, P. et al. The virtual brain: a simulator of primate brain network dynamics. Front. Neuroinformat. 7, 10 (2013).
Google Scholar
Stimberg, M., Goodman, D. F. M., Benichoux, V. & Brette, R. Equation-oriented specification of neural models for simulations. Front. Neuroinform 8, 6 (2014).
Google Scholar
Zenke, F. & Gerstner, W. Limits to high-speed simulations of spiking neural networks using general-purpose computers. Front. Neuroinform. 8, 76 (2014).
Google Scholar
Stefanini, F., Sheik, S., Neftci, E. & Indiveri, G. Pyncs: a microkernel for high-level definition and configuration of neuromorphic electronic systems. Front. Neuroinform. 8, 73 (2014).
Google Scholar
Hazan, H. et al. BindsNET: a machine learning-oriented spiking neural networks library in python. Front. Neuroinform. 12, 89 (2018).
Google Scholar
Mozafari, M., Ganjtabesh, M., Nowzari-Dalini, A. & Masquelier, T. Spyketorch: efficient simulation of convolutional spiking neural networks with at most one spike per neuron. Front. Neurosci. 13, 625 (2019).
Google Scholar
Sheik, S., Lenz, G., Bauer, F. & Kuepelioglu, N. SINABS: a simple Pytorch based SNN library specialised for Speck. GitHub https://github.com/synsense/sinabs (2019).
Buller, B. PySNN: a Python framework for spiking neural networks. GitHub https://github.com/BasBuller/PySNN (2019).
Qu, P., Zhang, Y., Fei, X. & Zheng, W. High performance simulation of spiking neural network on GPGPUs. IEEE Trans. Parallel Distrib. Syst. 31, 2510–2523 (2020).
Google Scholar
Yamaura, H., Igarashi, J. & Yamazaki, T. Simulation of a human-scale cerebellar network model on the K computer. Front. Neuroinform. 14, 16 (2020).
Google Scholar
Stimberg, M., Goodman, D. F. M. & Nowotny, T. Brian2GeNN: accelerating spiking neural network simulations with graphics hardware. Sci. Rep. 10, 410 (2020).
Google Scholar
Pehle, C. & Pedersen, J. E. Norse—a deep learning library for spiking neural networks. Zenodo https://doi.org/10.5281/zenodo.4422025 (2021).
Chen, G., Scherr, F. & Maass, W. A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing. Sci. Adv 8, eabq7592 (2022).
Google Scholar
Wang, C. et al. BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife 12, e86365 (2023).
Google Scholar
Zeng, Y. et al. Braincog: a spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired ai and brain simulation. Patterns 4, 100789 (2023).
Google Scholar
Williams, M. G. K., Plank, P. & Shrestha, S. B. Lava—a software framework for neuromorphic computing. GitHub https://github.com/lava-nc/lava (2023).
Amirsoleimani, A. et al. In-memory vector-matrix multiplication in monolithic complementary metal–oxide–semiconductor–memristor integrated circuits: design choices, challenges, and perspectives. Adv. Intell. Syst. 2, 2000115 (2020).
Google Scholar
Kwon, D. et al. Efficient hybrid training method for neuromorphic hardware using analog nonvolatile memory. IEEE Trans. Neural Netw. Learn. Syst. 36, 807–819 (2023).
Google Scholar
Koickal, T. J. et al. Analog VLSI circuit implementation of an adaptive neuromorphic olfaction chip. IEEE Trans. Circuits Syst. I 54, 60–73 (2007).
Google Scholar
Chicca, E. et al. A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Trans. Neural Netw. 14, 1297–1307 (2003).
Google Scholar
Giulioni, M. et al. A VLSI network of spiking neurons with plastic fully configurable stop-learning synapses. In Proc. IEEE International Conference on Electronics Circuits and Systems 678–681 (IEEE, 2008).
Chicca, E. et al. A multichip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Trans. Circuits Syst. I 54, 981–993 (2007).
Google Scholar
Vogelstein, R., Mallik, J. U., Culurciello, E., Cauwengberghs, G. & Etienne-Cummings, R. A multichip neuromorphic system for spike-based visual information processing. Neural Comput. 19, 2281–2300 (2007).
Google Scholar
