| 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 | 120 | 2018 |
| Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system S Schmitt, J Klähn, G Bellec, A Grübl, M Guettler, A Hartel, S Hartmann, ... 2017 International Joint Conference on Neural Networks (IJCNN), 2227-2234, 2017 | 70 | 2017 |
| Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017 | 50 | 2017 |
| 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 | 29 | 2019 |
| Memory-efficient deep learning on a SpiNNaker 2 prototype C Liu, G Bellec, B Vogginger, D Kappel, J Partzsch, F Neumärker, ... Frontiers in neuroscience 12, 840, 2018 | 26 | 2018 |
| 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, 2020 | 20* | 2020 |
| Pattern representation and recognition with accelerated analog neuromorphic systems MA Petrovici, S Schmitt, J Klähn, D Stöckel, A Schroeder, G Bellec, J Bill, ... 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, 2017 | 15 | 2017 |
| Advances in Neural Information Processing Systems G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass | 6 | 2018 |
| Classification with deep neural networks on an accelerated analog neuromorphic system S Schmitt, J Klhn, G Bellec, A Grbl, M Gttler, A Hartel, S Hartmann, ... arXiv (cit. on pp. 16, 113), 2016 | 5 | 2016 |
| Creating audio based experiments as social web games with the casimir framework D Wolff, G Bellec, A Friberg, A MacFarlane, T Weyde Audio Engineering Society Conference: 53rd International Conference …, 2014 | 3 | 2014 |
| Spike-frequency adaptation provides a long short-term memory to networks of spiking neurons D Salaj, A Subramoney, C Kraisnikovic, G Bellec, R Legenstein, W Maass bioRxiv, 2020 | 2 | 2020 |
| Eligibility traces provide a data-inspired alternative to backpropagation through time G Bellec, F Scherr, E Hajek, D Salaj, A Subramoney, R Legenstein, ... | 2 | 2019 |
| Slow feature analysis with spiking neurons and its application to audio stimuli G Bellec, M Galtier, R Brette, P Yger Journal of computational neuroscience 40 (3), 317-329, 2016 | 1 | 2016 |
| A social network integrated game experiment to relate tapping to speed perception and explore rhythm reproduction G Bellec, A Elowsson, A Friberg, D Wolff, T Weyde Proceedings of the Sound and Music Computing Conference, 19-26, 2013 | 1 | 2013 |
| A music similarity game prototype using the CASimIR API D Wolff, G Bellec, T Weyde Online Proc. of ISMIR Late Breaking Sessions, 2012 | 1 | 2012 |
| Slow processes of neurons enable a biologically plausible approximation to policy gradient A Subramoney, G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, ... 33nd NeurIPS workshop, 2019 | | 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
