{"id":4180,"date":"2024-09-29T22:55:12","date_gmt":"2024-09-30T02:55:12","guid":{"rendered":"https:\/\/cecas.clemson.edu\/ugvaidya\/?page_id=4180"},"modified":"2024-10-09T18:28:56","modified_gmt":"2024-10-09T22:28:56","slug":"information-flow-and-control-over-networks","status":"publish","type":"page","link":"https:\/\/cecas.clemson.edu\/ugvaidya\/information-flow-and-control-over-networks\/","title":{"rendered":"Information Flow and Control Over Networks"},"content":{"rendered":"\t\t
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Information Flow and Control Over Networks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\"causality-graph\"<\/figure><\/div>
\"influence-network\"<\/figure><\/div>
\"ocsillator-network\"<\/figure><\/div>
\"influential-nodes\"<\/figure><\/div>
\"information-flow\"<\/figure><\/div>\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t
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Information-based causality measure in a dynamical system<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Information flow and causality are the most fundamental concepts for analyzing and designing various engineering and natural sciences systems. A mathematically precise definition of information flow developed with dynamics in mind is necessary for the rigorous formulation of autonomy in a network dynamical system. The degree of interaction, as measured by the information flow between a network of autonomous agents and its environment, can be used to characterize the degree of autonomy in a network dynamical system. We have developed novel axiom-based formalism for information flow in network dynamical systems using ergodic theory and stochastic dynamics methods. The proposed formalism can be viewed as a natural extension of directed information from information theory to the dynamical system and is used to precisely characterize the flow of information and influence structure in a network dynamical system.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The problem of distributed control and estimation in large-scale dynamical network systems is intimately connected with the flow of information among network components. We are investigating the application of information transfer for reduced-order modeling of dynamical systems and for inferring causal structures from brain data.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\"information-routing\"<\/figure><\/div>
\"info-flow-maximize\"<\/figure><\/div>
\"neuro_-nfo-transfer\"<\/figure><\/div>
\"information-routing-control\"<\/figure><\/div>
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Control of information flow over networks<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes. It provides a better understanding of the contributions of these nodes, individually or collectively, towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework illustrates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that designing or reconfiguring the network topology can optimize the information transfer function between two chosen nodes.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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As a proof of concept, we apply our proposed methods in brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Selected publications<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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