Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions

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.

Automated summary

This study unifies the field-theoretical approach to neuronal networks with large deviations theory, deriving a rate function resembling Kullback-Leibler divergence through field theory to enable data-driven inference of model parameters and calculation of fluctuations beyond mean-field theory. Additionally, the study reveals a regime with fluctuation-induced transitions between mean-field solutions.