Mean field limit for disordered diffusions with singular interactions

Abstract

Motivated by considerations from neuroscience (macroscopic behavior of large ensembles of interacting neurons), we consider a population of mean field interacting diffusions in ${\mathbf{R}}^m$ in the presence of a random environment and with spatial extension: each diffusion is attached to one site of the lattice ${\mathbf{Z}}^d$, and the interaction between two diffusions is attenuated by a spatial weight that depends on their positions. For a general class of singular weights (including the case already considered in the physical literature when interactions obey to a power-law of parameter $0<\alpha <d$), we address the convergence as $N\to \infty$ of the empirical measure of the diffusions to the solution of a deterministic McKean-Vlasov equation and prove well-posedness of this equation, even in the degenerate case without noise. We provide also precise estimates of the speed of this convergence, in terms of an appropriate weighted Wasserstein distance, exhibiting in particular nontrivial fluctuations in the power-law case when $\frac{d}{2}\le \alpha <d$. Our framework covers the case of polynomially bounded monotone dynamics that are especially encountered in the main models of neural oscillators.

Published in at http://dx.doi.org/10.1214/13-AAP968 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Probabilistic Foundations of Spatial Mean-field Models in Ecology and Applications
• Emergence of oscillatory behaviors for excitable systems with noise and mean-field interaction, a slow-fast dynamics approach
• Local weak convergence for sparse networks of interacting processes
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• Large deviations for interacting diffusions with path-dependent McKean-Vlasov limit
• Locally interacting diffusions as space-time Markov random fields
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