| 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 31, 2018 | 291 | 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, ... Nature Communications 11 (3625), 2020 | 172 | 2020 |
| Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein ICLR 2018, 2017 | 157 | 2017 |
| 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 | 128 | 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 | 75 | 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 | 45 | 2018 |
| 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 | 18 | 2017 |
| 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 | 15* | 2021 |
| Local plasticity rules can learn deep representations using self-supervised contrastive predictions B Illing, J Ventura, G Bellec*, W Gerstner* Advances in Neural Information Processing Systems 34, 2021 | 9* | 2021 |
| Classification with deep neural networks on an accelerated analog neuromorphic system S Schmitt, J Klähn, G Bellec, A Grübl, M Güttler, A Hartel, S Hartmann, ... Proceedings of the 2017 IEEE International Joint Conference on Neural Networks, 2017 | 9 | 2017 |
| A solution to the learning dilemma for recurrent networks of spiking neurons Nat G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ... Commun 11 (1), 1, 2020 | 8 | 2020 |
| Eligibility traces provide a data-inspired alternative to backpropagation through time G Bellec, F Scherr, E Hajek, D Salaj, A Subramoney, R Legenstein, ... | 5 | 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 | 3 | 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 |
| Fitting summary statistics of neural data with a differentiable spiking network simulator G Bellec*, S Wang*, A Modirshanechi, J Brea, W Gerstner Advances in Neural Information Processing Systems 34, 2021 | 2 | 2021 |
| 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 30, 19-26, 2013 | 2 | 2013 |
| Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks A Subramoney, G Bellec, F Scherr, R Legenstein, W Maass bioRxiv, 2021 | 1 | 2021 |
| 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 | 1 | 2019 |
| 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 |
| 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 | 1 | |
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
