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Tom Tetzlaff

Showing results (1-10 of 22) with videos related to

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Plos Computational Biology|January 24, 2014
The correlation structure of local neuronal networks intrinsically results from recurrent dynamicsMoritz Helias, Tom Tetzlaff, Markus Diesmann
Frontiers in Computational Neuroscience|January 8, 2011
Rate Dynamics of Leaky Integrate-and-Fire Neurons with Strong SynapsesEilen Nordlie, Tom Tetzlaff, Gaute T Einevoll
Frontiers in Computational Neuroscience|October 24, 2013
A unified view on weakly correlated recurrent networksDmytro Grytskyy, Tom Tetzlaff, Markus Diesmann, et al.
Plos Computational Biology|August 26, 2020
Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer's diseaseClaudia Bachmann, Tom Tetzlaff, Renato Duarte, et al.
Frontiers in Neuroscience|January 10, 2022
Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight ResolutionStefan Dasbach, Tom Tetzlaff, Markus Diesmann, et al.
Plos Computational Biology|May 2, 2023
Coherent noise enables probabilistic sequence replay in spiking neuronal networksYounes Bouhadjar, Dirk J Wouters, Markus Diesmann, et al.
Plos Computational Biology|June 21, 2022
Sequence learning, prediction, and replay in networks of spiking neuronsYounes Bouhadjar, Dirk J Wouters, Markus Diesmann, et al.
Plos Computational Biology|November 8, 2012
Decorrelation of neural-network activity by inhibitory feedbackTom Tetzlaff, Moritz Helias, Gaute T Einevoll, et al.
Plos Computational Biology|November 14, 2014
Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the EEGKlas H Pettersen, Henrik Lindén, Tom Tetzlaff, 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.
Pageof 3

Showing results (1-10 of 22) with videos related to

Sort By:
Pageof 3
Plos Computational Biology|January 24, 2014
The correlation structure of local neuronal networks intrinsically results from recurrent dynamicsMoritz Helias, Tom Tetzlaff, Markus Diesmann
Frontiers in Computational Neuroscience|January 8, 2011
Rate Dynamics of Leaky Integrate-and-Fire Neurons with Strong SynapsesEilen Nordlie, Tom Tetzlaff, Gaute T Einevoll
Frontiers in Computational Neuroscience|October 24, 2013
A unified view on weakly correlated recurrent networksDmytro Grytskyy, Tom Tetzlaff, Markus Diesmann, et al.
Plos Computational Biology|August 26, 2020
Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer's diseaseClaudia Bachmann, Tom Tetzlaff, Renato Duarte, et al.
Frontiers in Neuroscience|January 10, 2022
Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight ResolutionStefan Dasbach, Tom Tetzlaff, Markus Diesmann, et al.
Plos Computational Biology|May 2, 2023
Coherent noise enables probabilistic sequence replay in spiking neuronal networksYounes Bouhadjar, Dirk J Wouters, Markus Diesmann, et al.
Plos Computational Biology|June 21, 2022
Sequence learning, prediction, and replay in networks of spiking neuronsYounes Bouhadjar, Dirk J Wouters, Markus Diesmann, et al.
Plos Computational Biology|November 8, 2012
Decorrelation of neural-network activity by inhibitory feedbackTom Tetzlaff, Moritz Helias, Gaute T Einevoll, et al.
Plos Computational Biology|November 14, 2014
Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the EEGKlas H Pettersen, Henrik Lindén, Tom Tetzlaff, 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.
Pageof 3