title: Controlling statistical moments of stochastic dynamical networks.
abstract: We consider a general class of stochastic networks and ask which network nodes need to be controlled, and how, to stabilize and switch between desired metastable (target) states in terms of the first and second statistical moments of the system. We first show that it is sufficient to directly interfere with a subset of nodes which can be identified using information about the graph of the network only. Then we develop a suitable method for feedback control which acts on that subset of nodes and preserves the covariance structure of the desired target state. Finally, we demonstrate our theoretical results using a stochastic Hopfield network and a global brain model. Our results are applicable to a variety of (model) networks and further our understanding of the relationship between network structure and collective dynamics for the benefit of effective control.
authors: Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115 Berlin, Germany.
authors: Dmytro
authors: D
authors: Bielievtsov
authors: Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115 Berlin, Germany.|Institut für Softwaretechnik und Theoretische Informatik, Technische Universität Berlin, Marchstraße 23, 10587 Berlin, Germany.
authors: Josef
authors: J
authors: Ladenbauer
authors: Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115 Berlin, Germany.|Institut für Softwaretechnik und Theoretische Informatik, Technische Universität Berlin, Marchstraße 23, 10587 Berlin, Germany.
authors: Klaus
authors: K
authors: Obermayer