| 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 |
| 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 |
| 2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022 | 41 | 2022 |
| Neuromorphic hardware learns to learn T Bohnstingl, F Scherr, C Pehle, K Meier, W Maass Frontiers in neuroscience 13, 483, 2019 | 33 | 2019 |
| Visualizing a joint future of neuroscience and neuromorphic engineering F Zenke, SM Bohté, C Clopath, IM Comşa, J Göltz, W Maass, ... Neuron 109 (4), 571-575, 2021 | 23 | 2021 |
| Reservoirs learn to learn A Subramoney, F Scherr, W Maass Reservoir Computing, 59-76, 2021 | 11 | 2021 |
| 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 |
| One-shot learning with spiking neural networks F Scherr, C Stöckl, W Maass BioRxiv, 2020 | 7 | 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 |
| Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. arXiv G Bellec, F Scherr, E Hajek, D Salaj, R Legenstein, W Maass arXiv preprint arXiv:1901.09049, 2019 | 5 | 2019 |
| CCN GAC Workshop: Issues with learning in biological recurrent neural networks LY Prince, E Boven, RH Eyono, A Ghosh, J Pemberton, F Scherr, ... arXiv preprint arXiv:2105.05382, 2021 | 2 | 2021 |
| Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task F Scherr, W Maass bioRxiv, 2021 | 2 | 2021 |
| Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse V1 G Chen, F Scherr, W Maass bioRxiv, 2021 | 1 | 2021 |
| 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 |
| Dimensionality and flexibility of learning in biological recurrent neural networks BA Richards, C Clopath, RP Costa, W Maass, LY Prince, A Ghosh, ... | 1 | 2020 |
| 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 |
| 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 | |
| Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece RH Eyono, E Boven, A Ghosh, J Pemberton, F Scherr, C Clopath, ... Neurons, Behavior, Data analysis, and Theory, 35302, 2022 | | 2022 |
| Role of feature selectivity in visual perturbation responses J Galván Fraile, F Scherr, W Maass, JJ Ramasco, CR Mirasso | | 2022 |
| Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece LY Prince, RH Eyono, E Boven, A Ghosh, J Pemberton, F Scherr, ... arXiv preprint arXiv:2105.05382, 2021 | | 2021 |
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
