On quantification and maximization of information transfer in network dynamical systems

Scientific Reports, vol. 13, pp. 5588

Abstract

Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and 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 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 brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.

Authors 4

1. Moirangthem Sailash Singh corresponding

   Indian Institute of Technology Madras

   Affiliation as printed

   Electrical Department, IIT Madras, Chennai, India. sailashm@gmail.com

   Electrical Department, IIT Madras, Chennai, India

2. Ramkrishna Pasumarthy

   Indian Institute of Technology Madras

   Affiliation as printed

   Electrical Department, IIT Madras, Chennai, India

3. Umesh Vaidya

   Clemson University

   Affiliation as printed

   Mechanical Department, Clemson University, Clemson, USA

4. Steffen K. Leonhardt Aachen Chair for Medical Information Technology

   RWTH Aachen University

   Affiliation as printed

   Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany