| Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017 | 166 | 2017 |
| STDP installs in winner-take-all circuits an online approximation to hidden Markov model learning D Kappel, B Nessler, W Maass PLoS computational biology 10 (3), e1003511, 2014 | 108 | 2014 |
| Network plasticity as Bayesian inference D Kappel, S Habenschuss, R Legenstein, W Maass PLoS computational biology 11 (11), e1004485, 2015 | 105 | 2015 |
| Recurrent spiking networks solve planning tasks E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters Scientific reports 6 (1), 1-10, 2016 | 58 | 2016 |
| A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning D Kappel, R Legenstein, S Habenschuss, M Hsieh, W Maass eneuro 5 (2), 2018 | 52 | 2018 |
| 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 |
| Adaptive extreme edge computing for wearable devices E Covi, E Donati, X Liang, D Kappel, H Heidari, M Payvand, W Wang Frontiers in Neuroscience, 429, 2021 | 28 | 2021 |
| Efficient reward-based structural plasticity on a SpiNNaker 2 prototype Y Yan, D Kappel, F Neumärker, J Partzsch, B Vogginger, S Höppner, ... IEEE transactions on biomedical circuits and systems 13 (3), 579-591, 2019 | 27 | 2019 |
| Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring D Kappel, S Habenschuss, R Legenstein, W Maass Advances in neural information processing systems 28, 2015 | 23 | 2015 |
| NEST 2.14. 0 A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ... Jülich Supercomputing Center, 2017 | 22 | 2017 |
| NEVESIM: event-driven neural simulation framework with a Python interface D Pecevski, D Kappel, Z Jonke Frontiers in neuroinformatics 8, 70, 2014 | 22 | 2014 |
| Structural plasticity on an accelerated analog neuromorphic hardware system S Billaudelle, B Cramer, MA Petrovici, K Schreiber, D Kappel, ... Neural networks 133, 11-20, 2021 | 19 | 2021 |
| Attention on abstract visual reasoning L Hahne, T Lüddecke, F Wörgötter, D Kappel arXiv preprint arXiv:1911.05990, 2019 | 12 | 2019 |
| Reward-based stochastic self-configuration of neural circuits D Kappel, R Legenstein, S Habenschuss, M Hsieh, W Maass arXiv preprint arXiv:1704.04238, 2017 | 9 | 2017 |
| Embodied synaptic plasticity with online reinforcement learning J Kaiser, M Hoff, A Konle, JC Vasquez Tieck, D Kappel, D Reichard, ... Frontiers in Neurorobotics 13, 81, 2019 | 8 | 2019 |
| CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling Z Yu, D Kappel, R Legenstein, S Song, F Chen, W Maass arXiv preprint arXiv:1606.00157, 2016 | 6 | 2016 |
| A synapse-centric account of the free energy principle D Kappel, C Tetzlaff arXiv preprint arXiv:2103.12649, 2021 | 2 | 2021 |
| Event-based Neural Network for ECG Classification with Delta Encoding and Early Stopping M Jobst, C Liu, J Partzsch, Y Yan, D Kappel, HA Gonzalez, Y Ji, ... 2020 6th International Conference on Event-Based Control, Communication, and …, 2020 | 2 | 2020 |
| EGRU: Event-based GRU for activity-sparse inference and learning A Subramoney, KK Nazeer, M Schöne, C Mayr, D Kappel arXiv preprint arXiv:2206.06178, 2022 | | 2022 |
| Differential Hebbian learning with time-continuous signals for active noise reduction K Möller, D Kappel, M Tamosiunaite, C Tetzlaff, B Porr, F Wörgötter Plos one 17 (5), e0266679, 2022 | | 2022 |
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
