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David Dahmen
David Dahmen
Inst. for Neuroscience and Medicine (INM-6), Research Centre Jülich
Bestätigte E-Mail-Adresse bei fz-juelich.de
Titel
Zitiert von
Zitiert von
Jahr
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
762016
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
462019
Correlated fluctuations in strongly coupled binary networks beyond equilibrium
D Dahmen, H Bos, M Helias
Physical Review X 6 (3), 031024, 2016
402016
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
292017
Statistical Field Theory for Neural Networks
M Helias, D Dahmen
Springer, 2020
252020
NEST 2.14. 0
A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ...
Jülich Supercomputing Center, 2017
222017
Functional methods for disordered neural networks
J Schücker, S Goedeke, D Dahmen, M Helias
arXiv preprint arXiv:1605.06758, 2016
212016
NEST 2.18. 0
J Jordan, R Deepu, J Mitchell, JM Eppler, S Spreizer, J Hahne, ...
Jülich Supercomputing Center, 2019
172019
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
132020
Transient chaotic dimensionality expansion by recurrent networks
C Keup, T Kühn, D Dahmen, M Helias
Physical Review X 11 (2), 021064, 2021
92021
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
92020
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
92016
Statistical field theory for neural networks
M Helias, D Dahmen
arXiv preprint arXiv:1901.10416, 2019
72019
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
52021
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
52021
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
52020
Capacity of the covariance perceptron
D Dahmen, M Gilson, M Helias
Journal of Physics A: Mathematical and Theoretical 53 (35), 354002, 2020
42020
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
32015
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
22021
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