David Dahmen
David Dahmen
Inst. for Neuroscience and Medicine & Inst. for Advanced Simulation, Research Centre Jülich
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Cited by
Cited by
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
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
Statistical field theory for neural networks
M Helias, D Dahmen
Springer, 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
Correlated fluctuations in strongly coupled binary networks beyond equilibrium
D Dahmen, H Bos, M Helias
Physical Review X 6 (3), 031024, 2016
NEST 2.18. 0
J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ...
Jülich Supercomputing Center, 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
NEST 2.14. 0
A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ...
Jülich Supercomputing Center, 2017
Functional methods for disordered neural networks
J Schücker, S Goedeke, D Dahmen, M Helias
arXiv preprint arXiv:1605.06758, 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
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
Transient chaotic dimensionality expansion by recurrent networks
C Keup, T Kühn, D Dahmen, M Helias
Physical Review X 11 (2), 021064, 2021
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
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
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
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
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
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
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
Capacity of the covariance perceptron
D Dahmen, M Gilson, M Helias
Journal of Physics A: Mathematical and Theoretical 53 (35), 354002, 2020
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