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100 1 _ |a van Meegen, Alexander
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245 _ _ |a Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
260 _ _ |a College Park, Md.
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520 _ _ |a 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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700 1 _ |a Kühn, Tobias
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700 1 _ |a Helias, Moritz
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773 _ _ |a 10.1103/PhysRevLett.127.158302
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856 4 _ |u https://juser.fz-juelich.de/record/902076/files/Invoice_21_AUG_006584-1.pdf
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