| Six networks on a universal neuromorphic computing substrate T Pfeil, A Grübl, S Jeltsch, E Müller, P Müller, MA Petrovici, M Schmuker, ... Frontiers in neuroscience 7, 2013 | 181 | 2013 |
| Deep learning with spiking neurons: Opportunities & challenges M Pfeiffer, T Pfeil Frontiers in Neuroscience 12, 774, 2018 | 172 | 2018 |
| A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems D Brüderle, MA Petrovici, B Vogginger, M Ehrlich, T Pfeil, S Millner, ... Biological cybernetics 104 (4), 263-296, 2011 | 117 | 2011 |
| Is a 4-bit synaptic weight resolution enough?–constraints on enabling spike-timing dependent plasticity in neuromorphic hardware T Pfeil, TC Potjans, S Schrader, W Potjans, J Schemmel, M Diesmann, ... Frontiers in Neuroscience 6, 2012 | 95 | 2012 |
| A neuromorphic network for generic multivariate data classification M Schmuker, T Pfeil, MP Nawrot Proceedings of the National Academy of Sciences 111 (6), 2081-2086, 2014 | 84 | 2014 |
| Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study T Pfeil, J Jordan, T Tetzlaff, A Grübl, J Schemmel, M Diesmann, K Meier Physical Review X 6 (2), 021023, 2016 | 22 | 2016 |
| Neuromorphic learning towards nano second precision T Pfeil, AC Scherzer, J Schemmel, K Meier The 2013 International Joint Conference on Neural Networks (IJCNN), 1-5, 2013 | 15 | 2013 |
| 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 | 14 | 2017 |
| The streaming rollout of deep networks-towards fully model-parallel execution V Fischer, J Köhler, T Pfeil Advances in Neural Information Processing Systems, 4039-4050, 2018 | 7 | 2018 |
| Exploring the potential of brain-inspired computing T Pfeil | 6 | 2015 |
| Configuration strategies for neurons and synaptic learning in large-scale neuromorphic hardware systems T Pfeil Diploma thesis (English), University of Heidelberg, HD-KIP 11-34, 2011 | 6 | 2011 |
| Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks A Kugele, T Pfeil, M Pfeiffer, E Chicca Frontiers in Neuroscience 14, 2020 | 3 | 2020 |
| Fast sampling with neuromorphic hardware MA Petrovici, D Stöckel, I Bytschok, J Bill, T Pfeil, J Schemmel, K Meier arXiv preprint arXiv:1311.3211, 2013 | 3 | 2013 |
| Neural Networks as Sources of uncorrelated Noise for functional neuralSystems J Jordan, I Bytschok, T Tetzlaff, T Pfeil, O Breitwieser, J Bill, M Diesmann, ... INM Retreat 2014, 2014 | 2 | 2014 |
| ItNet: iterative neural networks with tiny graphs for accurate and efficient anytime prediction T Pfeil arXiv preprint arXiv:2101.08685, 2021 | | 2021 |
| Classification of multivariate data with a spiking neural network on neuromorphic hardware M Schmuker, T Pfeil, MP Nawrot BMC Neuroscience 14 (1), 1-1, 2013 | | 2013 |
| A spiking classifier for nonlinear problems implemented on a neuromorphic hardware system M Schmuker, S Schrader, T Pfeil, MP Nawrot Frontiers in Computational Neuroscience, 183, 2012 | | 2012 |
| Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks Open Website A Kugele, T Pfeil, M Pfeiffer, E Chicca | | |
Karlheinz MeierRuprecht-Karls-Universität HeidelbergVerified email at kip.uni-heidelberg.de
