| PhD Viva

Name of the Speaker: Mr. Moirangthem Sailash Singh (EE17D017)
Guide: Prof. Ramakrishna Pasumarthy
Co-Guide: Dr. Steffen Leonhardt
Venue: ESB-244 (Seminar Hall)
Online meeting link: https://meet.google.com/djm-podc-fps
Date/Time: 3rd July 2024 (Wednesday), 4:30 PM
Title: Causal Inferences and Control in Network Systems: An Information Theoretic Approach

Abstract :

Complex network dynamics are prevalent in a wide range of biological and engineered network systems. The network's topology, in conjunction with its dynamics, which specifies interactions among the various nodes, governs the emergence of various causal interactions and information flows among these nodes, which are quantified by finding the difference between the rate of change in the differential entropy. However, the intricate relationship between network structure, dynamics, and information transfer function is not well understood, and the ability to manipulate network dynamics or the structure to attain specific information flows or functional patterns represents an ongoing and significant challenge. We integrate theories from information science with network control theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates 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 by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of simulated brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons. We identify the mechanism that defines the evolution of various functional patterns, characterized by information flows, in response to modifications in the underlying network dynamics. Furthermore, we present techniques for computing the optimal adjustments specified by control inputs, which lead to the attainment of the intended information routing patterns among the different nodes in both finite and infinite time horizons. This optimization problem can be efficiently resolved using non-linear programming tools and permits the simultaneous assignment of multiple desired patterns at different instances. We establish the algebraic and graph-theoretic conditions necessary to ensure the feasibility and stability of information routing patterns. We illustrate the routing mechanisms and control methods for attaining desired patterns in both biological oscillatory dynamics and engineered wireless communication systems.