Large-Deviation Approach to Random Recurrent Neuronal Networks : Parameter Inference and Fluctuation-Induced Transitions
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: 10.1103/PhysRevLett.127.158302
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2021
- Creator
-
van Meegen, Alexander
Kühn, Tobias
Helias, Moritz
- DOI
-
10.18154/RWTH-2021-11205
- URN
-
urn:nbn:de:101:1-2021120702391729229995
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:25 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
- van Meegen, Alexander
- Kühn, Tobias
- Helias, Moritz
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2021
Other Objects (12)
Momentum-dependence in the infinitesimal Wilsonian renormalization group
Phase transitions in classical systems : anisotropic models, computational methods, and universality predictions
Expansion of the effective action around non-Gaussian theories
Corrigendum: Expansion of the effective action around non-Gaussian theories (2018 J. Phys. A: Math. Theor. 51 375004)
Recurrence and external sources differentially shape network correlations
Optimal Sequence Memory in Driven Random Networks
The transfer function of the LIF model: from white to filtered noise
Interactions on structured networks
A statistical perspective on learning of time series in neural networks
Calcium current improves coincidence detection of the LIF model
Towards a unified theory of correlations in recurrent neural networks
Neurons hear their echo
Momentum-dependence in the infinitesimal Wilsonian renormalization group
Phase transitions in classical systems : anisotropic models, computational methods, and universality predictions
Expansion of the effective action around non-Gaussian theories
Corrigendum: Expansion of the effective action around non-Gaussian theories (2018 J. Phys. A: Math. Theor. 51 375004)
Recurrence and external sources differentially shape network correlations
Optimal Sequence Memory in Driven Random Networks
The transfer function of the LIF model: from white to filtered noise
Interactions on structured networks
A statistical perspective on learning of time series in neural networks
Calcium current improves coincidence detection of the LIF model
Towards a unified theory of correlations in recurrent neural networks
Neurons hear their echo
Momentum-dependence in the infinitesimal Wilsonian renormalization group
Phase transitions in classical systems : anisotropic models, computational methods, and universality predictions
Expansion of the effective action around non-Gaussian theories
Corrigendum: Expansion of the effective action around non-Gaussian theories (2018 J. Phys. A: Math. Theor. 51 375004)
Recurrence and external sources differentially shape network correlations
Optimal Sequence Memory in Driven Random Networks
The transfer function of the LIF model: from white to filtered noise
Interactions on structured networks
A statistical perspective on learning of time series in neural networks
Calcium current improves coincidence detection of the LIF model
Towards a unified theory of correlations in recurrent neural networks