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Monte Carlo simulations of carbon nanotube networks for optoelectronic applications., , , and . NANOARCH, page 135-136. IEEE Computer Society/ACM, (2014)Exploiting the short-term to long-term plasticity transition in memristive nanodevice learning architectures., , , , , and . IJCNN, page 947-954. IEEE, (2016)Use of Magnetoresistive Random-Access Memory as Approximate Memory for Training Neural Networks., , and . ICECS, page 553-556. IEEE, (2018)Hardware Spiking Neural Networks: Slow Tasks Resilient Learning with Longer Term-Memory Bits., , , , , and . BioCAS, page 1-4. IEEE, (2019)Toward Hardware Spiking Neural Networks with Mixed-Signal Event-Based Learning Rules., , , , and . IJCNN, page 1-8. IEEE, (2020)Flexible design methodology for spike encoding implementation on FPGA., , , and . BioCAS, page 379-383. IEEE, (2022)Implications of the Use of Magnetic Tunnel Junctions as Synapses in Neuromorphic Systems., , , and . ACM Great Lakes Symposium on VLSI, page 317-320. ACM, (2017)Preliminary Results Towards Reinforcement Learning with Mixed-Signal Memristive Neuromorphic Circuits., , and . ISCAS, page 1-5. IEEE, (2019)Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction., , , , , , , and . NANOARCH, page 1-5. IEEE, (2019)