TY  - JOUR
AU  - van Meegen, Alexander
AU  - Kühn, Tobias
AU  - Helias, Moritz
TI  - Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
JO  - Physical review letters
VL  - 127
IS  - 15
SN  - 0031-9007
CY  - College Park, Md.
PB  - APS
M1  - FZJ-2021-04016
SP  - 158302
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 34678014
UR  - <Go to ISI:>//WOS:000705650600007
DO  - DOI:10.1103/PhysRevLett.127.158302
UR  - https://juser.fz-juelich.de/record/902076
ER  -