13,937 research outputs found
The geometry of spontaneous spiking in neuronal networks
The mathematical theory of pattern formation in electrically coupled networks
of excitable neurons forced by small noise is presented in this work. Using the
Freidlin-Wentzell large deviation theory for randomly perturbed dynamical
systems and the elements of the algebraic graph theory, we identify and analyze
the main regimes in the network dynamics in terms of the key control
parameters: excitability, coupling strength, and network topology. The analysis
reveals the geometry of spontaneous dynamics in electrically coupled network.
Specifically, we show that the location of the minima of a certain continuous
function on the surface of the unit n-cube encodes the most likely activity
patterns generated by the network. By studying how the minima of this function
evolve under the variation of the coupling strength, we describe the principal
transformations in the network dynamics. The minimization problem is also used
for the quantitative description of the main dynamical regimes and transitions
between them. In particular, for the weak and strong coupling regimes, we
present asymptotic formulas for the network activity rate as a function of the
coupling strength and the degree of the network. The variational analysis is
complemented by the stability analysis of the synchronous state in the strong
coupling regime. The stability estimates reveal the contribution of the network
connectivity and the properties of the cycle subspace associated with the graph
of the network to its synchronization properties. This work is motivated by the
experimental and modeling studies of the ensemble of neurons in the Locus
Coeruleus, a nucleus in the brainstem involved in the regulation of cognitive
performance and behavior
Variance Analysis of Randomized Consensus in Switching Directed Networks
In this paper, we study the asymptotic properties of distributed consensus
algorithms over switching directed random networks. More specifically, we focus
on consensus algorithms over independent and identically distributed, directed
Erdos-Renyi random graphs, where each agent can communicate with any other
agent with some exogenously specified probability . While it is well-known
that consensus algorithms over Erdos-Renyi random networks result in an
asymptotic agreement over the network, an analytical characterization of the
distribution of the asymptotic consensus value is still an open question. In
this paper, we provide closed-form expressions for the mean and variance of the
asymptotic random consensus value, in terms of the size of the network and the
probability of communication . We also provide numerical simulations that
illustrate our results.Comment: 6 pages, 3 figures, submitted to American Control Conference 201
Graphs and networks theory
This chapter discusses graphs and networks theory
Consensus problems in networks of agents with switching topology and time-delays
In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results
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