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Journal Article | FZJ-2021-04016 |
; ;
2021
APS
College Park, Md.
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Please use a persistent id in citations: http://hdl.handle.net/2128/28916 doi:10.1103/PhysRevLett.127.158302
Abstract: 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.
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