| Hybrid scheme for modeling local field potentials from point-neuron networks E Hagen, D Dahmen, ML Stavrinou, H Lindén, T Tetzlaff, SJ van Albada, ... Cerebral Cortex, 1-36, 2016 | 76 | 2016 |
| Second type of criticality in the brain uncovers rich multiple-neuron dynamics D Dahmen, S Grün, M Diesmann, M Helias Proceedings of the National Academy of Sciences 116 (26), 13051-13060, 2019 | 46 | 2019 |
| Correlated fluctuations in strongly coupled binary networks beyond equilibrium D Dahmen, H Bos, M Helias Physical Review X 6 (3), 031024, 2016 | 40 | 2016 |
| Integration of continuous-time dynamics in a spiking neural network simulator J Hahne, D Dahmen, J Schuecker, A Frommer, M Bolten, M Helias, ... Frontiers in neuroinformatics 11, 34, 2017 | 29 | 2017 |
| Statistical Field Theory for Neural Networks M Helias, D Dahmen Springer, 2020 | 25 | 2020 |
| NEST 2.14. 0 A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ... Jülich Supercomputing Center, 2017 | 22 | 2017 |
| Functional methods for disordered neural networks J Schücker, S Goedeke, D Dahmen, M Helias arXiv preprint arXiv:1605.06758, 2016 | 21 | 2016 |
| NEST 2.18. 0 J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ... Jülich Supercomputing Center, 2019 | 17 | 2019 |
| The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks M Gilson, D Dahmen, R Moreno-Bote, A Insabato, M Helias PLOS Computational Biology 16 (10), e1008127, 2020 | 13 | 2020 |
| Transient chaotic dimensionality expansion by recurrent networks C Keup, T Kühn, D Dahmen, M Helias Physical Review X 11 (2), 021064, 2021 | 9 | 2021 |
| Self-consistent formulations for stochastic nonlinear neuronal dynamics J Stapmanns, T Kühn, D Dahmen, T Luu, C Honerkamp, M Helias Physical Review E 101 (4), 042124, 2020 | 9 | 2020 |
| Distributions of covariances as a window into the operational regime of neuronal networks D Dahmen, M Diesmann, M Helias arXiv preprint arXiv:1605.04153, 2016 | 9 | 2016 |
| Statistical field theory for neural networks M Helias, D Dahmen arXiv preprint arXiv:1901.10416, 2019 | 7 | 2019 |
| Strong and localized coupling controls dimensionality of neural activity across brain areas D Dahmen, S Recanatesi, X Jia, GK Ocker, L Campagnola, T Jarsky, ... bioRxiv, 2020.11. 02.365072, 2021 | 5 | 2021 |
| Global organization of neuronal activity only requires unstructured local connectivity D Dahmen, M Layer, L Deutz, PA Dąbrowska, N Voges, M von Papen, ... bioRxiv, 2020.07. 15.205013, 2021 | 5* | 2021 |
| On the complexity of resting state spiking activity in monkey motor cortex PA Dąbrowska, N Voges, M von Papen, J Ito, D Dahmen, A Riehle, ... Cerebral Cortex Communications 2 (3), tgab033, 2021 | 5 | 2021 |
| Unfolding recurrence by Green’s functions for optimized reservoir computing S Nestler, C Keup, D Dahmen, M Gilson, H Rauhut, M Helias Advances in Neural Information Processing Systems 33, 17380-17390, 2020 | 5 | 2020 |
| Capacity of the covariance perceptron D Dahmen, M Gilson, M Helias Journal of Physics A: Mathematical and Theoretical 53 (35), 354002, 2020 | 4 | 2020 |
| Hybrid scheme for modeling local field potentials from point-neuron networks E Hagen, D Dahmen, M Stavrinou, H Lindén, T Tetzlaff, S van Albada, ... BMC Neuroscience 16 (1), P67, 2015 | 3 | 2015 |
| Event-based update of synapses in voltage-based learning rules J Stapmanns, J Hahne, M Helias, M Bolten, M Diesmann, D Dahmen Frontiers in neuroinformatics 15, 2021 | 2 | 2021 |
