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@article{faucris.308923623,
abstract = {We consider the long time asymptotic behavior of a large system of N linear differential equations with random coefficients. We allow for general elliptic correlation structures among the coefficients, thus we substantially generalize our previous work (SIAM J. Math. Anal. 50 (2018) 3271–3290) that was restricted to the independent case. In particular, we analyze a recent model in the theory of neural networks (Phys. Rev. E 97 (2018) 062314) that specifically focused on the effect of the distributional asymmetry in the random connectivity matrix X. We rigorously prove and slightly correct the explicit formula from (J. Math. Phys. 41 (2000) 3233–3256) on the time decay as a function of the asymmetry parameter. Our main tool is an asymptotically precise formula for the normalized trace of f(X)g(X^{∗}), in the large N limit, where f and g are analytic functions.},
author = {Erdos, László and Krüger, Torben and Renfrew, David},
doi = {10.1214/22-AAP1886},
faupublication = {yes},
journal = {Annals of Applied Probability},
keywords = {matrix Dyson equation (MDE); Non-Hermitian random matrix; partially symmetric correlation; time evolution of neural networks},
note = {CRIS-Team Scopus Importer:2023-08-11},
pages = {3098-3144},
peerreviewed = {Yes},
title = {{RANDOMLY} {COUPLED} {DIFFERENTIAL} {EQUATIONS} {WITH} {ELLIPTIC} {CORRELATIONS}},
volume = {33},
year = {2023}
}