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Physical Review. E
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January 21, 2026
Field theory for optimal signal propagation in residual networks
Kirsten Fischer, David Dahmen, Moritz Helias
Elife
|
January 26, 2023
Signal denoising through topographic modularity of neural circuits
Barna 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 dynamics
David 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 rates
Torbjørn 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 networks
Matthieu Gilson, David Dahmen, Rubén Moreno-Bote, et al.
Arxiv
|
December 9, 2024
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks
Yuxiu Shao, David Dahmen, Stefano Recanatesi, et al.
Physical Review. E
|
May 20, 2020
Self-consistent formulations for stochastic nonlinear neuronal dynamics
Jonas Stapmanns, Tobias Kühn, David Dahmen, et al.
Frontiers in Neuroinformatics
|
June 28, 2021
Event-Based Update of Synapses in Voltage-Based Learning Rules
Jonas Stapmanns, Jan Hahne, Moritz Helias, et al.
Physical Review Letters
|
May 6, 2022
Gell-Mann-Low Criticality in Neural Networks
Lorenzo 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.
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Search research articles
Search
Showing results (1-10 of 15) with videos related to
Sort By:
Page
of 2
Physical Review. E
|
January 21, 2026
Field theory for optimal signal propagation in residual networks
Kirsten Fischer, David Dahmen, Moritz Helias
Elife
|
January 26, 2023
Signal denoising through topographic modularity of neural circuits
Barna 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 dynamics
David 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 rates
Torbjørn 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 networks
Matthieu Gilson, David Dahmen, Rubén Moreno-Bote, et al.
Arxiv
|
December 9, 2024
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks
Yuxiu Shao, David Dahmen, Stefano Recanatesi, et al.
Physical Review. E
|
May 20, 2020
Self-consistent formulations for stochastic nonlinear neuronal dynamics
Jonas Stapmanns, Tobias Kühn, David Dahmen, et al.
Frontiers in Neuroinformatics
|
June 28, 2021
Event-Based Update of Synapses in Voltage-Based Learning Rules
Jonas Stapmanns, Jan Hahne, Moritz Helias, et al.
Physical Review Letters
|
May 6, 2022
Gell-Mann-Low Criticality in Neural Networks
Lorenzo 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.
Page
of 2