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Learning through structure: towards deep neuromorphic knowledge graph embeddings., , , and . CoRR, (2021)Fast and energy-efficient neuromorphic deep learning with first-spike times., , , , , , , , , and 2 other author(s). Nat. Mach. Intell., 3 (9): 823-835 (2021)Live Demonstration: Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate., , , , , , , , , and 15 other author(s). ISCAS, page 1. IEEE, (2020)Stochasticity from function - Why the Bayesian brain may need no noise., , , , , , , , and . Neural Networks, (2019)An energy-based model for neuro-symbolic reasoning on knowledge graphs., and . ICMLA, page 916-921. IEEE, (2021)Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate., , , , , , , , , and 15 other author(s). ISCAS, page 1-5. IEEE, (2020)Relational representation learning with spike trains.. IJCNN, page 1-8. IEEE, (2022)Generative models on accelerated neuromorphic hardware., , , , , , , , , and 14 other author(s). CoRR, (2018)Stochasticity from function - why the Bayesian brain may need no noise., , , , , , , , and . CoRR, (2018)Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate., , , , , , , , , and 15 other author(s). CoRR, (2019)