TY  - EJOUR
AU  - van Meegen, Alexander
AU  - Kühn, Tobias
AU  - Helias, Moritz
TI  - Large Deviation Approach to Random Recurrent Neuronal Networks: Rate Function, Parameter Inference, and Activity Prediction
M1  - FZJ-2020-03976
PY  - 2020
AB  - 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.
LB  - PUB:(DE-HGF)25
UR  - https://juser.fz-juelich.de/record/885635
ER  -