| 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 | 458 | 2020 |
| 2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022 | 363 | 2022 |
| 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 | 105 | 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 | 70 | 2021 |
| Neuromorphic hardware learns to learn T Bohnstingl*, F Scherr*, C Pehle, K Meier, W Maass Frontiers in neuroscience 13, 483, 2019 | 54 | 2019 |
| Neuromorphic Comput DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Eng 2, 022501, 2022 | 35 | 2022 |
| Reservoirs learn to learn A Subramoney, F Scherr, W Maass Reservoir Computing: Theory, Physical Implementations, and Applications, 59-76, 2021 | 22 | 2021 |
| A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing G Chen, F Scherr, W Maass science advances 8 (44), eabq7592, 2022 | 20 | 2022 |
| One-shot learning with spiking neural networks F Scherr, C Stöckl, W Maass BioRxiv, 2020.06. 17.156513, 2020 | 19 | 2020 |
| 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 | 10* | 2021 |
| Eligibility traces provide a data-inspired alternative to backpropagation through time G Bellec*, F Scherr*, E Hajek, D Salaj, A Subramoney, R Legenstein, ... NeurIPS 2019 workshop "Real Neurons & Hidden Units: Future directions at the …, 2019 | 9 | 2019 |
| Self-supervised learning through efference copies F Scherr, Q Guo, T Moraitis Advances in Neural Information Processing Systems 35, 2022 | 8 | 2022 |
| Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task F Scherr, W Maass bioRxiv, 2021.11. 17.469025, 2021 | 4 | 2021 |
| Competition between bottom-up visual input and internal inhibition generates error neurons in a model of the mouse primary visual cortex JG Fraile, F Scherr, JJ Ramasco, A Arkhipov, W Maass, CR Mirasso bioRxiv, 2023.01. 27.525984, 2023 | 3 | 2023 |
| 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, 2021.01. 25.428153, 2021 | 3 | 2021 |
| 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 1, 2022 | 2 | 2022 |
| 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, 2021.12. 07.471653, 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, ... NeurIPS 2019 workshop "Biological and Artificial Reinforcement Learning", 2019 | 1 | 2019 |
| Fast learning without synaptic plasticity in spiking neural networks A Subramoney, G Bellec, F Scherr, R Legenstein, W Maass Scientific Reports 14 (1), 8557, 2024 | | 2024 |
Wolfgang MaassProfessor of Computer Science, Graz University of Technology在 igi.tugraz.at 的电子邮件经过验证
