%0 Journal Article
%A van Meegen, Alexander
%A Kühn, Tobias
%A Helias, Moritz
%T Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
%J Physical review letters
%V 127
%N 15
%@ 0031-9007
%C College Park, Md.
%I APS
%M FZJ-2021-04016
%P 158302
%D 2021
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 34678014
%U <Go to ISI:>//WOS:000705650600007
%R 10.1103/PhysRevLett.127.158302
%U https://juser.fz-juelich.de/record/902076