| Brain simulation as a cloud service: The Virtual Brain on EBRAINS M Schirner, L Domide, D Perdikis, P Triebkorn, L Stefanovski, R Pai, ... NeuroImage 251, 118973, 2022 | 71 | 2022 |
| Automatic generation of connectivity for large-scale neuronal network models through structural plasticity S Diaz-Pier, M Naveau, M Butz-Ostendorf, A Morrison Frontiers in neuroanatomy 10, 57, 2016 | 66 | 2016 |
| Code generation in computational neuroscience: a review of tools and techniques I Blundell, R Brette, TA Cleland, TG Close, D Coca, AP Davison, ... Frontiers in neuroinformatics 12, 68, 2018 | 50 | 2018 |
| Nest 2.12. 0 S Kunkel, R Deepu, HE Plesser, B Golosio, ME Lepperød, JM Eppler, ... Jülich Supercomputing Center, 2017 | 41 | 2017 |
| On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread M Hashemi, AN Vattikonda, V Sip, S Diaz-Pier, A Peyser, H Wang, ... PLoS computational biology 17 (7), e1009129, 2021 | 38 | 2021 |
| Regional changes of brain structure during progression of idiopathic Parkinson's disease–A longitudinal study using deformation based morphometry P Pieperhoff, M Südmeyer, L Dinkelbach, CJ Hartmann, S Ferrea, ... Cortex 151, 188-210, 2022 | 36 | 2022 |
| NEST 2.18. 0 J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ... Jülich Supercomputing Center, 2019 | 36 | 2019 |
| Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling OV Popovych, K Jung, T Manos, S Diaz-Pier, F Hoffstaedter, J Schreiber, ... NeuroImage 236, 118201, 2021 | 29 | 2021 |
| Toward rigorous parameterization of underconstrained neural network models through interactive visualization and steering of connectivity generation C Nowke, S Diaz-Pier, B Weyers, B Hentschel, A Morrison, TW Kuhlen, ... Frontiers in neuroinformatics 12, 32, 2018 | 25 | 2018 |
| NEST 2.14. 0 A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ... Jülich Supercomputing Center, 2017 | 25 | 2017 |
| NEST 2.16. 0 C Linssen, R Deepu, J Mitchell, ME Lepperød, J Garrido, S Spreizer, ... Jülich Supercomputing Center, 2018 | 20 | 2018 |
| Long-term desynchronization by coordinated reset stimulation in a neural network model with synaptic and structural plasticity T Manos, S Diaz-Pier, PA Tass Frontiers in physiology 12, 716556, 2021 | 19 | 2021 |
| NEST 2.10. 0 H Bos, R Deepu, T Stocco, M Schmidt, JM Eppler, F Michler, HE Plesser, ... JARA-HPC, 2015 | 18 | 2015 |
| NEST 3.0 J Hahne, R Deepu, A Morales-Gregorio, J Mitchell, JM Eppler, S Spreizer, ... Computational and Systems Neuroscience, 2021 | 14 | 2021 |
| Ensemble Kalman filter optimizing deep neural networks: an alternative approach to non-performing gradient descent A Yegenoglu, K Krajsek, SD Pier, M Herty Machine Learning, Optimization, and Data Science: 6th International …, 2020 | 11 | 2020 |
| Exploring parameter and hyper-parameter spaces of neuroscience models on high performance computers with learning to learn A Yegenoglu, A Subramoney, T Hater, C Jimenez-Romero, W Klijn, ... Frontiers in computational neuroscience 16, 885207, 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 |
| Brain modelling as a service: the virtual brain on EBRAINS M Schirner, L Domide, D Perdikis, P Triebkorn, L Stefanovski, R Pai, ... arXiv preprint arXiv:2102.05888, 2021 | 8 | 2021 |
| The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks H Lu, S Diaz, M Lenz, A Vlachos bioRxiv, 2023.03. 09.531681, 2023 | 7 | 2023 |
| RateML: A code generation tool for brain network models M van der Vlag, M Woodman, J Fousek, S Diaz-Pier, A Pérez Martín, ... Frontiers in network physiology 2, 826345, 2022 | 7 | 2022 |
