Statistical field theory for neural networks M Helias, D Dahmen Springer, 2020 | 108 | 2020 |
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 | 100 | 2019 |
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 | 100 | 2016 |
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 | 100 | 2015 |
Correlated fluctuations in strongly coupled binary networks beyond equilibrium D Dahmen, H Bos, M Helias Physical Review X 6 (3), 031024, 2016 | 46 | 2016 |
NEST 2.18. 0 J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ... Jülich Supercomputing Center, 2019 | 34 | 2019 |
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 | 33 | 2017 |
Global organization of neuronal activity only requires unstructured local connectivity D Dahmen, M Layer, L Deutz, PA Dąbrowska, N Voges, M von Papen, ... Elife 11, e68422, 2022 | 28 | 2022 |
Transient chaotic dimensionality expansion by recurrent networks C Keup, T Kühn, D Dahmen, M Helias Physical Review X 11 (2), 021064, 2021 | 28 | 2021 |
Functional methods for disordered neural networks J Schücker, S Goedeke, D Dahmen, M Helias arXiv preprint arXiv:1605.06758, 2016 | 28 | 2016 |
Strong coupling and local control of dimensionality across brain areas D Dahmen, S Recanatesi, GK Ocker, X Jia, M Helias, E Shea-Brown Biorxiv, 2020.11. 02.365072, 2020 | 26 | 2020 |
NEST 2.14. 0 A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ... Jülich Supercomputing Center, 2017 | 24 | 2017 |
Unified field theoretical approach to deep and recurrent neuronal networks K Segadlo, B Epping, A van Meegen, D Dahmen, M Krämer, M Helias Journal of Statistical Mechanics: Theory and Experiment 2022 (10), 103401, 2022 | 20 | 2022 |
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 | 20 | 2020 |
Gell-Mann–Low Criticality in Neural Networks L Tiberi, J Stapmanns, T Kühn, T Luu, D Dahmen, M Helias Physical Review Letters 128 (16), 168301, 2022 | 19 | 2022 |
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 | 19 | 2020 |
Decomposing neural networks as mappings of correlation functions K Fischer, A René, C Keup, M Layer, D Dahmen, M Helias Physical Review Research 4 (4), 043143, 2022 | 15 | 2022 |
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 | 15 | 2021 |
NEST 3.0 J Hahne, S Diaz, A Patronis, W Schenck, A Peyser, S Graber, S Spreizer, ... Zenedo. doi 10, 2021 | 14 | 2021 |
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 |