Locally Optimal Control of Complex Networks

Klickstein, Isaac; Shirin, Afroza; Sorrentino, Francesco

doi:10.1103/physrevlett.119.268301

Cited by 39 publications

(31 citation statements)

References 28 publications

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“…Possible solutions to this seemingly fundamental limitation have been proposed, but only for the linearized system (Klickstein, Shirin, & Sorrentino, 2017). On the other hand, the control of complex networks with non-linear dynamics, and particularly of adaptive networks, is a field still in its infancy.…”

Section: Steering Dynamicsmentioning

confidence: 99%

Neurofeedback: Principles, appraisal, and outstanding issues

Papo

2019

Eur J of Neuroscience

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Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far‐reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain‐behaviour relationships.

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Section: Steering Dynamicsmentioning

confidence: 99%

Neurofeedback: Principles, appraisal, and outstanding issues

Papo

2019

Eur J of Neuroscience

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“…Is there a more general approach to nonlinear network control which can be used for comparison with linear network control? We do not have an answer at the present, as the collective behaviors of nonlinear dynamical networks are extremely diverse, so are the possible control strategies [21,[32][33][34][35][36][37][38][39]. However, regardless of the type of nonlinear control, heterogeneity in the nodal importance ranking can be anticipated in general, due to the interplay between the nonlinear nodal dynamics and network structure.…”

Section: Is It Possible To Use Linear Controllability As a Kind Of Cementioning

confidence: 92%

“…Control of real world complex networks based on the rules of nonlinear dynamics has remained to be an extremely difficult problem. Existing strategies include local pinning [32][33][34][35], feedback vertex set control [36][37][38], controlled switch among coexisting attractors [39], or local control [21]. These methods belong to the category of openloop control, i.e., one applies pre-defined control signals or parameter perturbations to a feedback vertex set chosen according to some physical criteria.…”

Section: Introductionmentioning

confidence: 99%

Irrelevance of linear controllability to nonlinear dynamical networks

Jiang

Lai

2019

Nat Commun

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There has been tremendous development in linear controllability of complex networks. Real-world systems are fundamentally nonlinear. Is linear controllability relevant to nonlinear dynamical networks? We identify a common trait underlying both types of control: the nodal “importance”. For nonlinear and linear control, the importance is determined, respectively, by physical/biological considerations and the probability for a node to be in the minimum driver set. We study empirical mutualistic networks and a gene regulatory network, for which the nonlinear nodal importance can be quantified by the ability of individual nodes to restore the system from the aftermath of a tipping-point transition. We find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former large-degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly the irrelevance of linear controllability to these systems. The recent claim of successful application of linear controllability to Caenorhabditis elegans connectome is examined and discussed.

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“…Our work goes beyond the existing literature in the following three aspects. Firstly, in spite of the tremendous recent interest in linear controllability of complex networks and in controlling nonlinear dynamical networks [71][72][73][74][75][76][77][78], the idea of remote control has not appeared in any previous work. Secondly, our idea of control exploits the effects of dynamical adaptation on the robustness of multilayer networks, which have not been studied in the literature.…”

Section: Introductionmentioning

confidence: 99%

Remote control of cascading dynamics on complex multilayer networks

2019

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To develop effective control strategies to enhance the robustness of multilayer networks against largescale failures is of significant value. We articulate the idea of 'remote control' whereby adaptive perturbations to one network layer are able to enhance the resilience of not only itself but also other interconnected network layers. We analyze the principle of remote control using percolation dynamics by showing analytically and numerically that, with the adaptive generation of a small number of new links in the control layer, not only is this layer but also other layers become dramatically more resistant to cascading failures. We also find that remote control is more effective for scale-free than for random networks. Remote intervention of multilayer network systems through adaptation has real-world applications, which we illustrate using the rail and coach transportation system in the Great Britain.

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Locally Optimal Control of Complex Networks

Cited by 39 publications

References 28 publications

Neurofeedback: Principles, appraisal, and outstanding issues

Neurofeedback: Principles, appraisal, and outstanding issues

Irrelevance of linear controllability to nonlinear dynamical networks

Remote control of cascading dynamics on complex multilayer networks

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