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Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses., , , , , , , , и . IEEE J. Emerg. Sel. Topics Circuits Syst., 8 (1): 77-85 (2018)End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays., , , , , , , , и . VLSI-SoC, стр. 1-5. IEEE, (2022)Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators., , , , , , , , и . IRPS, стр. 3. IEEE, (2022)A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making., , , , , , и . ISCAS, стр. 1-5. IEEE, (2020)Brain-inspired recurrent neural network with plastic RRAM synapses., , и . ISCAS, стр. 1-5. IEEE, (2018)Neuromorphic computing with hybrid memristive/CMOS synapses for real-time learning., , , , и . ISCAS, стр. 1386-1389. IEEE, (2016)Low-energy inference machine with multilevel HfO2 RRAM arrays., , , , , , и . ESSDERC, стр. 174-177. IEEE, (2019)Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks., , , , , , , и . ISCAS, стр. 1-5. IEEE, (2018)Towards a Universal Model of Dielectric Breakdown., , , , , и . IRPS, стр. 1-8. IEEE, (2023)Optimized programming algorithms for multilevel RRAM in hardware neural networks., , , , , , , , и . IRPS, стр. 1-6. IEEE, (2021)