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%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
}