Large Deviations of a Network of Neurons with Dynamic Sparse Random Connections

TL;DR

This paper establishes a large deviation principle for a model of interacting neurons with sparse, random, and unscaled connections, incorporating noise, and provides a theoretical framework relevant to neuroscience and social networks.

Contribution

It introduces a process-level large deviation principle for a lattice-based neuron network with random sparse connections, combining large particle limits with random graph theory.

Findings

Derived a process-level LDP for the neuron network model.

Connected large deviations with random graph and matrix theories.

Applicable to neuroscience and social network modeling.

Abstract

In this work we determine a process-level Large Deviation Principle (LDP) for a model of interacting particles indexed by a lattice $\mathbb{Z}^d$. The connections are random, sparse and unscaled, so that the system converges in the large size limit due to the probability of a connection between any two particles decreasing as the system size increases. The particles are also subject to noise (such as independent Brownian Motions). The method of proof is to assume a process-level (or Level 3) LDP for the double-layer empirical measure for the noise and connections, and then apply a series of transformations to this to obtain an LDP for the process-level empirical measure of our system. Although it is not explicitly necessary, we expect that most applications of this work should involve an assumption of stationarity of the probability law for the noise and connections under translations of the lattice, so that the system converged to an ergodic probability law in the large size limit. This work synthesizes the theory of large-size limits of interacting particles with that of random graphs and matrices. It should therefore be relevant to neuroscience and social networks theory in particular.