| Long short-term memory and learning-to-learn in networks of spiking neurons G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass Advances in Neural Information Processing Systems, 787-797, 2018 | 73 | 2018 |
| Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets G Bellec, F Scherr, E Hajek, D Salaj, R Legenstein, W Maass arXiv preprint arXiv:1901.09049, 2019 | 20 | 2019 |
| A solution to the learning dilemma for recurrent networks of spiking neurons G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ... bioRxiv, 738385, 2019 | 3 | 2019 |
| Eligibility traces provide a data-inspired alternative to backpropagation through time G Bellec, F Scherr, E Hajek, D Salaj, A Subramoney, R Legenstein, ... | | 2019 |
| A solution to the learning dilemma for recurrent G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ... | | |
| Supplementary materials for: A solution to the learning dilemma for recurrent networks of spiking neurons G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ... | | |
| Biologically inspired alternatives to backpropagation through time for learning in recurrent neural networks G Bellec, F Scherr, D Salaj, E Hajek, R Legenstein, W Maass | | |
| Supplementary information for: Long short-term memory and learning-to-learn in networks of spiking neurons G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass | | |
Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
