- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
In: https://doi.org/10.1103/PhysRevLett.121.058301
In: https://doi.org/10.1073/pnas.2023832118
In: https://doi.org/10.1103/PhysRevLett.125.088103
Bonn, Rheinische Friedrich-Wilhelms-Universität, Dissertation, 2022
- Keyword
-
Nervennetz
Modell
Neuronales Netz
Hirnfunktion
Pulsverarbeitendes neuronales Netz
Visuelles System
Hebbsche Lernregel
- Event
-
Veröffentlichung
- (where)
-
Bonn
- (who)
-
Universitäts- und Landesbibliothek Bonn
- (when)
-
2022
- Creator
- Contributor
-
Memmesheimer, Raoul-Martin
Metsch, Bernard
- Handle
-
20.500.11811/10062
- URN
-
urn:nbn:de:hbz:5-66816
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:34 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kalle Kossio, Felipe Yaroslav
- Memmesheimer, Raoul-Martin
- Metsch, Bernard
- Universitäts- und Landesbibliothek Bonn
Time of origin
- 2022
Other Objects (12)
The relevance of network micro-structure for neural dynamics
Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure
Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics
Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics
The influence of network structure on neuronal network dynamics
Neural Network Dynamics During Socio-Emotional Cue-Integration
Artificial neural network regression models: Predicting GDP growth
Memory association dynamics on neural network with dynamic synapses
An Evaluation of the Dynamics of Diluted Neural Network
The influence of network structure on neuronal dynamics
Neural dynamics and network topology interact to form critical avalanches
CODEX, a neural network approach to explore signaling dynamics landscapes
The relevance of network micro-structure for neural dynamics
Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure
Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics
Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics
The influence of network structure on neuronal network dynamics
Neural Network Dynamics During Socio-Emotional Cue-Integration
Artificial neural network regression models: Predicting GDP growth
Memory association dynamics on neural network with dynamic synapses
An Evaluation of the Dynamics of Diluted Neural Network
The influence of network structure on neuronal dynamics
Neural dynamics and network topology interact to form critical avalanches
CODEX, a neural network approach to explore signaling dynamics landscapes
The relevance of network micro-structure for neural dynamics
Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure
Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics
Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics
The influence of network structure on neuronal network dynamics
Neural Network Dynamics During Socio-Emotional Cue-Integration
Artificial neural network regression models: Predicting GDP growth
Memory association dynamics on neural network with dynamic synapses
An Evaluation of the Dynamics of Diluted Neural Network
The influence of network structure on neuronal dynamics
Neural dynamics and network topology interact to form critical avalanches