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Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity., , , and . Front. Comput. Neurosci., (2014)Deep counter networks for asynchronous event-based processing., , and . CoRR, (2016)Training Deep Spiking Neural Networks using Backpropagation., , and . CoRR, (2016)Precise deep neural network computation on imprecise low-power analog hardware., , , , and . CoRR, (2016)Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks., , and . SAC, page 293-298. ACM, (2016)Spatio-temporal Spike Pattern Classification in Neuromorphic Systems., , , and . Living Machines, volume 8064 of Lecture Notes in Computer Science, page 262-273. Springer, (2013)Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs., , and . ECML, volume 4701 of Lecture Notes in Computer Science, page 250-261. Springer, (2007)Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker., , , , , and . IJCNN, page 1-8. IEEE, (2015)Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision., , , and . GCPR, volume 13024 of Lecture Notes in Computer Science, page 297-312. Springer, (2021)Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing, , , , and . 2022 IEEE Radar Conference (RadarConf22), Piscataway, IEEE, (2022)