On Quantification and Control of Information Transfers in Complex Dynamical Networks

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Name of the Speaker: Moirangthem Sailash Singh (EE17D017)
Name of the Guide: Dr. Ramkrishna Pasumarthy
Date/Time: 12th October 2022, 3.00pm

Information flow or transfer in a dynamical network refers to the flow of information from a source entity to a receiver. It quantifies the contribution of an event towards the occurrence of another event, thereby enabling us to determine which event will have greater accuracy in predicting the outcome of a different event. The theory of information transfer has various applications in the field of biological science such as studying the flow of information from one neuron to another, understanding the effect of protein concentrations in gene

regulatory networks, and the causal inference between the entity and the options markets. Other applications are in the field of climate science, turbulence research, network dynamics and oscillatory systems. In our work, we focus on quantifying the information transfers among nodes in network dynamical systems. Our work is also focused on the optimal control of the information transfer to any desired value over a finite time horizon. The underlying idea behind the control techniques is to control the degree of uncertainties using external inputs. We show that, for a given desired information transfer, the values of the random state variables can only be distributed within a specific set of probability distributions. To optimally steer the

information transfer to the desired value, we convert the control problem into a nonlinear program, which can be solved numerically. We demonstrate our theory and control approach using a gene regulatory system, where we quantify and control the contribution of a cell to the protein concentration level of a neighbouring cell.