arXiv subjects

Lee DeVille

Publications and source records attributed to Lee DeVille.

22 records · Page 2Linked to original sources

arXiv2013

Synchrony and Periodicity in an Excitable Stochastic Neural Network with Multiple Subpopulations

We consider a fully stochastic excitatory neuronal network with a number of subpopulations with different firing rates. We show that as network size goes to infinity, this limits on a deterministic hybrid model whose trajectories are discontinuous. The jumps in the limit correspond to large synchronous events that involve a large proportion of the network. We also perform a rigorous analysis of the limiting deterministic system in certain cases, and show that it displays synchrony and periodicity in a large region of parameter space.

math.DS↗

arXiv2013

Modular dynamical systems on networks

We propose a new framework for the study of continuous time dynamical systems on networks. We view such dynamical systems as collections of interacting control systems. We show that a class of maps between graphs called graph fibrations give rise to maps between dynamical systems on networks. This allows us to produce conjugacy between dynamical systems out of combinatorial data. In particular we show that surjective graph fibrations lead to synchrony subspaces in networks. The injective graph fibrations, on the other hand, give rise to surjective maps from large dynamical systems to smaller ones. One can view these surjections as a kind of "fast/slow" variable decompositions or as "abstractions" in the computer science sense of the word.

math.DS↗

arXiv2013

Spectral Theory for Networks with Attractive and Repulsive Interactions

There is a wealth of applied problems that can be posed as a dynamical system defined on a network with both attractive and repulsive interactions. Some examples include: understanding synchronization properties of nonlinear oscillator;, the behavior of groups, or cliques, in social networks; the study of optimal convergence for consensus algorithm; and many other examples. Frequently the problems involve computing the index of a matrix, i.e. the number of positive and negative eigenvalues, and the dimension of the kernel. In this paper we consider one of the most common examples, where the matrix takes the form of a signed graph Laplacian. We show that the there are topological constraints on the index of the Laplacian matrix related to the dimension of a certain homology group. In certain situations, when the homology group is trivial, the index of the operator is rigid and is determined only by the topology of the network and is independent of the strengths of the interactions. In general these constraints give upper and lower bounds on the number of positive and negative eigenvalues, with the dimension of the homology group counting the number of eigenvalue crossings. The homology group also gives a natural decomposition of the dynamics into "fixed" degrees of freedom, whose index does not depend on the edge-weights, and an orthogonal set of "free" degrees of freedom, whose index changes as the edge weights change. We also present some numerical studies of this problem for large random matrices.

math.SP↗

arXiv2011

Fully Synchronous Solutions and the Synchronization Phase Transition for the Finite N Kuramoto Model

We present a detailed analysis of the stability of synchronized solutions to the Kuramoto system of oscillators. We derive an analytical expression counting the dimension of the unstable manifold associated to a given stationary solution. From this we are able to derive a number of consequences, including: analytic expressions for the first and last frequency vectors to synchronize, upper and lower bounds on the probability that a randomly chosen frequency vector will synchronize, and very sharp results on the large $N$ limit of this model. One of the surprises in this calculation is that for frequencies that are Gaussian distributed the correct scaling for full synchrony is not the one commonly studied in the literature---rather, there is a logarithmic correction to the scaling which is related to the extremal value statistics of the random frequency vector.

math.DS↗