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cs.NE2025
A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
Maximilian Baronig, Yeganeh Bahariasl, Ozan Özdenizci +1
Recurrent spiking neural networks (RSNNs) can be implemented very efficiently in neuromorphic systems. Nevertheless, training of these models with powerful gradient-based learning…
cs.NE2025
Adversarially Robust Spiking Neural Networks with Sparse Connectivity
Mathias Schmolli, Maximilian Baronig, Robert Legenstein +1
Deployment of deep neural networks in resource-constrained embedded systems requires innovative algorithmic solutions to facilitate their energy and memory efficiency. To further e…
cs.NE2024
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos +7
There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at d…