Large-Deviation Approach to Random Recurrent Neuronal Networks : Parameter Inference and Fluctuation-Induced Transitions
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: 10.1103/PhysRevLett.127.158302
- Ereignis
-
Veröffentlichung
- (wo)
-
Aachen
- (wer)
-
Universitätsbibliothek der RWTH Aachen
- (wann)
-
2021
- Urheber
-
van Meegen, Alexander
Kühn, Tobias
Helias, Moritz
- DOI
-
10.18154/RWTH-2021-11205
- URN
-
urn:nbn:de:101:1-2021120702391729229995
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:25 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- van Meegen, Alexander
- Kühn, Tobias
- Helias, Moritz
- Universitätsbibliothek der RWTH Aachen
Entstanden
- 2021
Ähnliche Objekte (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)
The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
Optimal Sequence Memory in Driven Random Networks
Recurrence and external sources differentially shape network correlations
Calcium current improves coincidence detection of the LIF model
A statistical perspective on learning of time series in neural networks
Interactions on structured networks
The transfer function of the LIF model: from white to filtered noise
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)
The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
Optimal Sequence Memory in Driven Random Networks
Recurrence and external sources differentially shape network correlations
Calcium current improves coincidence detection of the LIF model
A statistical perspective on learning of time series in neural networks
Interactions on structured networks
The transfer function of the LIF model: from white to filtered noise
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)
The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
Optimal Sequence Memory in Driven Random Networks
Recurrence and external sources differentially shape network correlations
Calcium current improves coincidence detection of the LIF model
A statistical perspective on learning of time series in neural networks
Interactions on structured networks
The transfer function of the LIF model: from white to filtered noise