%0 Electronic Article
%A van Meegen, Alexander
%A Kühn, Tobias
%A Helias, Moritz
%T Large Deviation Approach to Random Recurrent Neuronal Networks: Rate Function, Parameter Inference, and Activity Prediction
%M FZJ-2020-03976
%D 2020
%X Statistical field theory captures collective non-equilibrium dynamics of neuronal networks, but it does not address the inverse problem of searching the connectivity to implement a desired dynamics. We here show for an analytically solvable network model that the effective action in statistical field theory is identical to the rate function in large deviation theory; using field theoretical methods we derive this rate function. It takes the form of a Kullback-Leibler divergence and enables data-driven inference of model parameters and Bayesian prediction of time series.
%F PUB:(DE-HGF)25
%9 Preprint
%U https://juser.fz-juelich.de/record/885635