1,744 research outputs found
Controllability and Fraction of Leaders in Infinite Network
In this paper, we study controllability of a network of linear
single-integrator agents when the network size goes to infinity. We first
investigate the effect of increasing size by injecting an input at every node
and requiring that network controllability Gramian remain well-conditioned with
the increasing dimension. We provide theoretical justification to the intuition
that high degree nodes pose a challenge to network controllability. In
particular, the controllability Gramian for the networks with bounded maximum
degrees is shown to remain well-conditioned even as the network size goes to
infinity. In the canonical cases of star, chain and ring networks, we also
provide closed-form expressions which bound the condition number of the
controllability Gramian in terms of the network size. We next consider the
effect of the choice and number of leader nodes by actuating only a subset of
nodes and considering the least eigenvalue of the Gramian as the network size
increases. Accordingly, while a directed star topology can never be made
controllable for all sizes by injecting an input just at a fraction of
nodes; for path or cycle networks, the designer can actuate a non-zero fraction
of nodes and spread them throughout the network in such way that the least
eigenvalue of the Gramians remain bounded away from zero with the increasing
size. The results offer interesting insights on the challenges of control in
large networks and with high-degree nodes.Comment: 6 pages, 3 figures, to appear in 2014 IEEE CD
Control of Multilayer Networks
The controllability of a network is a theoretical problem of relevance in a
variety of contexts ranging from financial markets to the brain. Until now,
network controllability has been characterized only on isolated networks, while
the vast majority of complex systems are formed by multilayer networks. Here we
build a theoretical framework for the linear controllability of multilayer
networks by mapping the problem into a combinatorial matching problem. We found
that correlating the external signals in the different layers can significantly
reduce the multiplex network robustness to node removal, as it can be seen in
conjunction with a hybrid phase transition occurring in interacting Poisson
networks. Moreover we observe that multilayer networks can stabilize the fully
controllable multiplex network configuration that can be stable also when the
full controllability of the single network is not stable
Controllability of structural brain networks.
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function
General Theory of Topological Explanations and Explanatory Asymmetry
In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B. Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; a criterion for describing counterfactual dependencies in two explanatory modes, i.e. the vertical and the horizontal; and, finally, a third perspectival one that tells us when to use the vertical and when to use the horizontal mode. In the second step, I show how this general theory of topological explanations accounts for explanatory asymmetry in both the vertical and horizontal explanatory modes. Finally, in the third step, I argue that this theory is universally applicable across biological sciences, which helps to unify essential concepts of biological networks
A Framework to Control Functional Connectivity in the Human Brain
In this paper, we propose a framework to control brain-wide functional
connectivity by selectively acting on the brain's structure and parameters.
Functional connectivity, which measures the degree of correlation between
neural activities in different brain regions, can be used to distinguish
between healthy and certain diseased brain dynamics and, possibly, as a control
parameter to restore healthy functions. In this work, we use a collection of
interconnected Kuramoto oscillators to model oscillatory neural activity, and
show that functional connectivity is essentially regulated by the degree of
synchronization between different clusters of oscillators. Then, we propose a
minimally invasive method to correct the oscillators' interconnections and
frequencies to enforce arbitrary and stable synchronization patterns among the
oscillators and, consequently, a desired pattern of functional connectivity.
Additionally, we show that our synchronization-based framework is robust to
parameter mismatches and numerical inaccuracies, and validate it using a
realistic neurovascular model to simulate neural activity and functional
connectivity in the human brain.Comment: To appear in the proceedings of the 58th IEEE Conference on Decision
and Contro
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