| Spike frequency adaptation supports network computations on temporally dispersed information D Salaj, A Subramoney, C Kraisnikovic, G Bellec, R Legenstein, W Maass Elife 10, e65459, 2021 | 70* | 2021 |
| Fault pruning: Robust training of neural networks with memristive weights C Kraišniković, S Stathopoulos, T Prodromakis, R Legenstein International Conference on Unconventional Computation and Natural …, 2023 | 1 | 2023 |
| Machine learning classification of RR Lyrae stars observed by TESS L Steinwender, PG Beck, K Hambleton, C Kraisnikovic, ... TASC6/KASC13 Workshop, 2022 | 1 | 2022 |
| Spike-based symbolic computations on bit strings and numbers C Kraišniković, W Maass, R Legenstein Neuro-Symbolic Artificial Intelligence: The State of the Art 342, 214-234, 2022 | 1 | 2022 |
| Spike-based symbolic computations on symbol sequences C Kraisnikovic, W Maass, R Legenstein Bernstein Conference 2021, 2021 | | 2021 |
| Spike-frequency adaptation contributes long short-term memory to networks of spiking neurons A Subramoney, C Kraisnikovic, D Salaj, G Bellec, R Legenstein, W Maass 2020 Bernstein Conference, 2020 | | 2020 |
Robert LegensteinInstitute for Theoretical Computer Science, Graz University of Technology在 igi.tugraz.at 的电子邮件经过验证
