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David Dahmen

Showing results (1-10 of 15) with videos related to

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Physical Review. E|January 21, 2026
Field theory for optimal signal propagation in residual networksKirsten Fischer, David Dahmen, Moritz Helias
Elife|January 26, 2023
Signal denoising through topographic modularity of neural circuitsBarna Zajzon, David Dahmen, Abigail Morrison, et al.
Proceedings of the National Academy of Sciences of the United States of America|June 14, 2019
Second type of criticality in the brain uncovers rich multiple-neuron dynamicsDavid Dahmen, Sonja Grün, Markus Diesmann, et al.
Plos Computational Biology|April 14, 2025
On the validity of electric brain signal predictions based on population firing ratesTorbjørn V Ness, Tom Tetzlaff, Gaute T Einevoll, et al.
Plos Computational Biology|October 12, 2020
The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networksMatthieu Gilson, David Dahmen, Rubén Moreno-Bote, et al.
Arxiv|December 9, 2024
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networksYuxiu Shao, David Dahmen, Stefano Recanatesi, et al.
Physical Review. E|May 20, 2020
Self-consistent formulations for stochastic nonlinear neuronal dynamicsJonas Stapmanns, Tobias Kühn, David Dahmen, et al.
Frontiers in Neuroinformatics|June 28, 2021
Event-Based Update of Synapses in Voltage-Based Learning RulesJonas Stapmanns, Jan Hahne, Moritz Helias, et al.
Physical Review Letters|May 6, 2022
Gell-Mann-Low Criticality in Neural NetworksLorenzo Tiberi, Jonas Stapmanns, Tobias Kühn, et al.
Physical Review. E|June 16, 2022
Erratum: Self-consistent formulations for stochastic nonlinear neuronal dynamics [Phys. Rev. E 101, 042124 (2020)]Jonas Stapmanns, Tobias Kühn, David Dahmen, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
Physical Review. E|January 21, 2026
Field theory for optimal signal propagation in residual networksKirsten Fischer, David Dahmen, Moritz Helias
Elife|January 26, 2023
Signal denoising through topographic modularity of neural circuitsBarna Zajzon, David Dahmen, Abigail Morrison, et al.
Proceedings of the National Academy of Sciences of the United States of America|June 14, 2019
Second type of criticality in the brain uncovers rich multiple-neuron dynamicsDavid Dahmen, Sonja Grün, Markus Diesmann, et al.
Plos Computational Biology|April 14, 2025
On the validity of electric brain signal predictions based on population firing ratesTorbjørn V Ness, Tom Tetzlaff, Gaute T Einevoll, et al.
Plos Computational Biology|October 12, 2020
The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networksMatthieu Gilson, David Dahmen, Rubén Moreno-Bote, et al.
Arxiv|December 9, 2024
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networksYuxiu Shao, David Dahmen, Stefano Recanatesi, et al.
Physical Review. E|May 20, 2020
Self-consistent formulations for stochastic nonlinear neuronal dynamicsJonas Stapmanns, Tobias Kühn, David Dahmen, et al.
Frontiers in Neuroinformatics|June 28, 2021
Event-Based Update of Synapses in Voltage-Based Learning RulesJonas Stapmanns, Jan Hahne, Moritz Helias, et al.
Physical Review Letters|May 6, 2022
Gell-Mann-Low Criticality in Neural NetworksLorenzo Tiberi, Jonas Stapmanns, Tobias Kühn, et al.
Physical Review. E|June 16, 2022
Erratum: Self-consistent formulations for stochastic nonlinear neuronal dynamics [Phys. Rev. E 101, 042124 (2020)]Jonas Stapmanns, Tobias Kühn, David Dahmen, et al.
Pageof 2