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000902076 1001_ $$0P:(DE-Juel1)173607$$avan Meegen, Alexander$$b0$$eCorresponding author
000902076 245__ $$aLarge-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
000902076 260__ $$aCollege Park, Md.$$bAPS$$c2021
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000902076 520__ $$aWe here unify the field-theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we show that the effective action is identical to the rate function and derive the latter using field theory. This rate function takes the form of a Kullback-Leibler divergence which enables data-driven inference of model parameters and calculation of fluctuations beyond mean-field theory. Lastly, we expose a regime with fluctuation-induced transitions between mean-field solutions.
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000902076 7001_ $$0P:(DE-Juel1)164473$$aKühn, Tobias$$b1
000902076 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2
000902076 773__ $$0PERI:(DE-600)1472655-5$$a10.1103/PhysRevLett.127.158302$$gVol. 127, no. 15, p. 158302$$n15$$p158302$$tPhysical review letters$$v127$$x0031-9007$$y2021
000902076 8564_ $$uhttps://juser.fz-juelich.de/record/902076/files/Invoice_21_AUG_006584-1.pdf
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