David Dahmen
David Dahmen
Inst. for Neuroscience and Medicine (INM-6), Research Centre Jülich
Verified email at
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
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
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
Statistical Field Theory for Neural Networks
M Helias, D Dahmen
Springer, 2020
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
NEST 2.18. 0
J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ...
Jülich Supercomputing Center, 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
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
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
Transient chaotic dimensionality expansion by recurrent networks
C Keup, T Kühn, D Dahmen, M Helias
Physical Review X 11 (2), 021064, 2021
Statistical field theory for neural networks
M Helias, D Dahmen
arXiv preprint arXiv:1901.10416, 2019
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
Capacity of the covariance perceptron
D Dahmen, M Gilson, M Helias
Journal of Physics A: Mathematical and Theoretical 53 (35), 354002, 2020
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
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
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
26th Annual Computational Neuroscience Meeting (CNS* 2017): Part 2
LL Rubchinsky, S Ahn, W Klijn, B Cumming, S Yates, V Karakasis, ...
BMC neuroscience 18 (1), 59, 2017
Computing local field potentials based on spiking cortical networks
M Stavrinou, E Hagen, D Dahmen, H Lindén, T Tetzlaff, S Van Albada, ...
Front Neuroinformatics 8 (10.3389), 2014
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