Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions.
Phys Rev Lett
; 127(15): 158302, 2021 Oct 08.
Article
em En
| MEDLINE
| ID: mdl-34678014
ABSTRACT
We here unify the field-theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we show that the effective action is identical to the rate function and derive the latter using field theory. This rate function takes the form of a Kullback-Leibler divergence which enables data-driven inference of model parameters and calculation of fluctuations beyond mean-field theory. Lastly, we expose a regime with fluctuation-induced transitions between mean-field solutions.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Clinical_trials
Idioma:
En
Revista:
Phys Rev Lett
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Alemanha