Universität zu Köln

van Meegen, Alexander ORCID: 0000-0003-2766-3982, Kuehn, Tobias and Helias, Moritz ORCID: 0000-0002-0404-8656 (2021). Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions. Phys. Rev. Lett., 127 (15). COLLEGE PK: AMER PHYSICAL SOC. ISSN 1079-7114

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code van Meegen, Alexander UNSPECIFIED orcid.org/0000-0003-2766-3982 UNSPECIFIED Kuehn, Tobias UNSPECIFIED UNSPECIFIED UNSPECIFIED Helias, Moritz UNSPECIFIED orcid.org/0000-0002-0404-8656 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-595661
DOI:	10.1103/PhysRevLett.127.158302
Journal or Publication Title:	Phys. Rev. Lett.
Volume:	127
Number:	15
Date:	2021
Publisher:	AMER PHYSICAL SOC
Place of Publication:	COLLEGE PK
ISSN:	1079-7114
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language DYNAMICS; CHAOS Multiple languages Physics, Multidisciplinary Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/59566