%0 Generic %1 windhager2023snn %A Windhager, Daniel %A Moser, Bernhard A. %A Lunglmayr, Michael %D 2023 %K deep-learning neural-networks snn %R https://arxiv.org/abs/2311.14447 %T SNN Architecture for Differential Time Encoding Using Decoupled Processing Time %U https://arxiv.org/pdf/2311.14447

@misc{windhager2023snn, added-at = {2024-01-23T19:21:33.000+0100}, archiveprefix = {arXiv}, author = {Windhager, Daniel and Moser, Bernhard A. and Lunglmayr, Michael}, biburl = {https://www.bibsonomy.org/bibtex/2e924c8fd9fd6ae6102af75f24ec998fa/scch}, doi = {https://arxiv.org/abs/2311.14447}, eprint = {2311.14447}, interhash = {27d864d6909a057ce00ae3be9b51828a}, intrahash = {e924c8fd9fd6ae6102af75f24ec998fa}, keywords = {deep-learning neural-networks snn}, primaryclass = {eess.SP}, timestamp = {2024-01-23T19:21:33.000+0100}, title = {SNN Architecture for Differential Time Encoding Using Decoupled Processing Time}, url = {https://arxiv.org/pdf/2311.14447}, year = 2023 }