Copeland, B. The Manchester computer: a revised history part 2: the Baby computer. IEEE Ann. Hist. Comput. 33, 22–37 (2011).
Williams, R. S. What’s next? [The end of Moore’s law]. Comput. Sci. Eng. 19, 7–13 (2017).
Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).
Article PubMed PubMed Central Google Scholar
Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019).
Article CAS PubMed Google Scholar
Kumar, S., Williams, R. S. & Wang, Z. Third-order nanocircuit elements for neuromorphic engineering. Nature 585, 518–523 (2020).
Article CAS PubMed Google Scholar
Singh, A. et al. The design of analogue in-memory computing tiles. Nat. Electron. 8, 1156–1169 (2025).
Ielmini, D. & Wong, H.-S. P. In-memory computing with resistive switching devices. Nat. Electron. 1, 333–343 (2018).
Momeni, A. et al. Training of physical neural networks. Nature 645, 53–61 (2025).
Article CAS PubMed Google Scholar
Aguirre, F. et al. Hardware implementation of memristor-based artificial neural networks. Nat. Commun. 15, 1974 (2024).
Article CAS PubMed PubMed Central Google Scholar
Zhang, W. et al. Edge learning using a fully integrated neuro-inspired memristor chip. Science 381, 1205–1211 (2023).
Article CAS PubMed Google Scholar
Goswami, S. et al. Decision trees within a molecular memristor. Nature 597, 51–56 (2021).
Article CAS PubMed Google Scholar
Chen, S., Zhang, T., Tappertzhofen, S., Yang, Y. & Valov, I. Electrochemical-memristor-based artificial neurons and synapses—fundamentals, applications, and challenges. Adv. Mater. 35, 2301924 (2023).
Chen, S. et al. Electrochemical ohmic memristors for continual learning. Nat. Commun. 16, 2348 (2025).
Article CAS PubMed PubMed Central Google Scholar
Gaur, P. et al. Molecularly engineered memristors for reconfigurable neuromorphic functionalities. Adv. Mater. 38, e09143 (2026).
Comments (0)