| Extremely scalable spiking neural network simulation code: from laptops to exascale computers J Jordan, T Ippen, M Helias, I Kitayama, M Sato, J Igarashi, M Diesmann, ... Frontiers in Neuroinformatics 12, 2, 2018 | 171 | 2018 |
| Evolving interpretable plasticity for spiking networks J Jordan, M Schmidt, W Senn, MA Petrovici Elife 10, e66273, 2021 | 60 | 2021 |
| Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses WAM Wybo, J Jordan, B Ellenberger, UM Mengual, T Nevian, W Senn Elife 10, e60936, 2021 | 46 | 2021 |
| Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons P Haider, B Ellenberger, L Kriener, J Jordan, W Senn, MA Petrovici Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 45 | 2021 |
| NEST 2.18. 0 J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ... Jülich Supercomputing Center, 2019 | 44* | 2019 |
| NEST 2.12. 0 S Kunkel, A Morrison, P Weidel, JM Eppler, A Sinha, W Schenck Zenodo doi 10, 2017 | 44* | 2017 |
| 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 | 38 | 2016 |
| Learning cortical representations through perturbed and adversarial dreaming N Deperrois, MA Petrovici, W Senn, J Jordan Elife 11, e76384, 2022 | 37* | 2022 |
| NEST 2.20. 0 T Fardet, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, I Kitayama, ... Computational and Systems Neuroscience, 2020 | 37 | 2020 |
| Deterministic networks for probabilistic computing J Jordan, MA Petrovici, O Breitwieser, J Schemmel, K Meier, M Diesmann, ... Scientific Reports 9 (1), 1-17, 2019 | 34* | 2019 |
| A neuronal least-action principle for real-time learning in cortical circuits W Senn, D Dold, AF Kungl, B Ellenberger, J Jordan, Y Bengio, ... ELife 12, RP89674, 2024 | 32 | 2024 |
| NEST 2.14. 0 A Peyser, A Sinha, SB Vennemo, T Ippen, J Jordan, S Graber, A Morrison, ... Zenodo, 2017 | 29* | 2017 |
| NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways WAM Wybo, MC Tsai, VAK Tran, B Illing, J Jordan, A Morrison, W Senn Proceedings of the National Academy of Sciences 120 (32), e2300558120, 2023 | 26 | 2023 |
| Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers J Pronold, J Jordan, BJN Wylie, I Kitayama, M Diesmann, S Kunkel Parallel Computing 113, 102952, 2022 | 23 | 2022 |
| A closed-loop toolchain for neural network simulations of learning autonomous agents J Jordan, P Weidel, A Morrison Frontiers in Computational Neuroscience 13, 46, 2019 | 22* | 2019 |
| A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations J Albers, J Pronold, AC Kurth, SB Vennemo, KH Mood, A Patronis, ... Frontiers in neuroinformatics 16, 2022 | 20 | 2022 |
| NEST 2.16. 0 C Linssen, R Deepu, J Mitchell, ME Lepperød, J Garrido, S Spreizer, ... Jülich Supercomputing Center, 2018 | 20 | 2018 |
| Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring J Pronold, J Jordan, BJN Wylie, I Kitayama, M Diesmann, S Kunkel Frontiers in neuroinformatics 15, 2021 | 17 | 2021 |
| NEST 3.0 J Hahne, S Diaz, A Patronis, W Schenck, A Peyser, S Graber, S Spreizer, ... Zenedo. doi 10, 2021 | 16 | 2021 |
| NEST 3.3 S Spreizer, J Mitchell, J Jordan, W Wybo, A Kurth, SB Vennemo, J Pronold, ... Version, 2022 | 12 | 2022 |
Mihai A. PetroviciGroup Leader, NeuroTMA Lab, University of BernVerified email at unibe.ch
