| Dendritic computing: branching deeper into machine learning J Acharya, A Basu, R Legenstein, T Limbacher, P Poirazi, X Wu Neuroscience 489, 275-289, 2022 | 44 | 2022 |
| Emergence of stable synaptic clusters on dendrites through synaptic rewiring T Limbacher, R Legenstein Frontiers in computational neuroscience 14, 57, 2020 | 27 | 2020 |
| H-mem: Harnessing synaptic plasticity with hebbian memory networks T Limbacher, R Legenstein Advances in Neural Information Processing Systems 33, 21627-21637, 2020 | 20 | 2020 |
| Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity T Limbacher, O Özdenizci, R Legenstein arXiv preprint arXiv:2205.11276, 2022 | 3 | 2022 |
| Context-dependent computations in spiking neural networks with apical modulation R Ferrand, M Baronig, T Limbacher, R Legenstein International Conference on Artificial Neural Networks, 381-392, 2023 | 2 | 2023 |
| Rapid learning with phase-change memory-based in-memory computing through learning-to-learn T Ortner, H Petschenig, A Vasilopoulos, R Renner, Š Brglez, T Limbacher, ... Nature Communications 16 (1), 1243, 2025 | | 2025 |
| Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing T Ortner, H Petschenig, A Vasilopoulos, R Renner, Š Brglez, T Limbacher, ... arXiv preprint arXiv:2405.05141, 2024 | | 2024 |
| Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity T Limbacher, O Özdenizci, R Legenstein IEEE Transactions on Neural Networks and Learning Systems, 2023 | | 2023 |
| Supervised Learning Algorithms for Spiking Neuromorphic Hardware T Limbacher | | 2017 |
| Supplementary information for: H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks T Limbacher, R Legenstein | | |
Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at