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 | 140 | 2018 |
NEST 2.12. 0 S Kunkel, A Morrison, P Weidel, JM Eppler, A Sinha, W Schenck Zenodo doi 10, 2017 | 42* | 2017 |
Evolving interpretable plasticity for spiking networks J Jordan, M Schmidt, W Senn, MA Petrovici Elife 10, e66273, 2021 | 41 | 2021 |
NEST 2.18. 0 J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ... Jülich Supercomputing Center, 2019 | 38* | 2019 |
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 | 34 | 2016 |
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 | 33 | 2021 |
NEST 2.20. 0 T Fardet, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, I Kitayama, ... Computational and Systems Neuroscience, 2020 | 33 | 2020 |
Learning cortical representations through perturbed and adversarial dreaming N Deperrois, MA Petrovici, W Senn, J Jordan Elife 11, e76384, 2022 | 27* | 2022 |
Deterministic networks for probabilistic computing J Jordan, MA Petrovici, O Breitwieser, J Schemmel, K Meier, M Diesmann, ... Scientific Reports 9 (1), 1-17, 2019 | 27* | 2019 |
NEST 2.14. 0 A Peyser, A Sinha, SB Vennemo, T Ippen, J Jordan, S Graber, A Morrison, ... Zenodo, 2017 | 27* | 2017 |
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 | 25 | 2021 |
NEST 2.16. 0 C Linssen, R Deepu, J Mitchell, ME Lepperød, J Garrido, S Spreizer, ... Jülich Supercomputing Center, 2018 | 20 | 2018 |
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 | 18* | 2019 |
NEST 3.0 J Hahne, S Diaz, A Patronis, W Schenck, A Peyser, S Graber, S Spreizer, ... Zenedo. doi 10, 2021 | 14 | 2021 |
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 | 10 | 2022 |
NEST 3.3 S Spreizer, J Mitchell, J Jordan, W Wybo, A Kurth, SB Vennemo, J Pronold, ... Version, 2022 | 10 | 2022 |
Efficient communication in distributed simulations of spiking neuronal networks with gap junctions J Jordan, M Helias, M Diesmann, S Kunkel Frontiers in Neuroinformatics 14, 12, 2020 | 10 | 2020 |
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 | 9 | 2022 |
NEST 2.8. 0 JM Eppler, R Deepu, C Bachmann, T Zito, A Peyser, J Jordan, R Pauli, ... JARA-HPC, 2015 | 9 | 2015 |
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 | 8 | 2021 |