Papers citing Norse

8. Papers citing Norse#

Norse is actively applied in the literature and is presently cited by more than 50 papers. We are keeping track of a few papers that cite Norse below. Please get in touch with us if we missed a paper. Contact details can be found in the page About Norse.

  • Eike-Manuel Bansbach, Alexander von Bank, and Laurent Schmalen. Spiking neural network decision feedback equalization. ArXiv, 2022. URL:

  • Ding Chen, Peixi Peng, Tiejun Huang, and Yonghong Tian. Deep reinforcement learning with spiking q-learning. CoRR, 2022. URL:, arXiv:2201.09754.

  • Julien Dupeyroux. A toolbox for neuromorphic sensing in robotics. ArXiv, 2021.

  • Rida El-Allami, Alberto Marchisio, Muhammad Shafique, and Ihsen Alouani. Securing deep spiking neural networks against adversarial attacks through inherent structural parameters. In Proceedings of the 24th Conference on Design, Automation and Test in Europe, DATE '21. 2021. URL:

  • Davide L Manna, Alex Vicente-Sola, Paul Kirkland, Trevor J Bihl, and Gaetano Di Caterina. Frameworks for snns: a review of data science-oriented software and an expansion of spyketorch. In International Conference on Engineering Applications of Neural Networks, 227–238. Springer, 2023.

  • Eric Müller, Elias Arnold, Oliver Julien Breitwieser, Milena Czierlinski, Arne Emmel, Jakob Kaiser, Christian Mauch, Sebastian Schmitt, Philipp Spilger, Raphael Stock, Yannik Stradmann, Johannes Weis, Andreas Baumbach, Sebastian Billaudelle, Benjamin Cramer, Falk Leonard Ebert, Julian Göltz, Joscha Ilmberger, Vitali Karasenko, Mitja Kleider, Aron Leibfried, Christian Pehle, and Johannes Schemmel. A scalable approach to modeling on accelerated neuromorphic hardware. Frontiers in Neuroscience, 2022. URL:

  • Jens Egholm Pedersen and Jörg Conradt. Aestream: accelerated event-based processing with coroutines. Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference, 2022. URL:

  • Mechislav M Pugavko, Oleg V Maslennikov, and Vladimir I Nekorkin. Multitask computation through dynamics in recurrent spiking neural networks. Scientific Reports, 13(1):3997, 2023.

  • Xiaohe Xue, Ralf D. Wimmer, Michael M. Halassa, and Zhe Sage Chen. Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation. Cognitive Computation, Feb 2022. URL:, doi:10.1007/s12559-022-09994-2.

We refer to lists from Semantic Scholar and Google Scholar for the complete list of references.