Grant: Discovery Grants Program - Individual

Traditional information theory is concerned with the flow of information through a channel, from sender to receiver. However, in distributed networks of interacting elements, any element (or set of elements) can be considered as a sender or a receiver. In such cases, when there is no specific channel of interest, and so no primary senders or receivers, how can we apply the principles of information theory to study the network? Integrated information attempts to capture an intuitive notion - a physical system integrates information if it specifies more information as a whole than when its parts are considered independently. Originally developed in neuroscience, integrated information is an attempt to capture the balance between segregation and integration observed in the brain. Individual brain regions appear to perform specialized functions (segregation) yet work together to achieve a unified response (integration). In addition to applications in neuroscience, integrated information has been employed as a general measure of complexity, finding applications across the natural sciences, including biology, computer science, and physics. During the next five years I will concurrently pursue three avenues for advancing the field of integrated information: �Develop the theoretical understand of integrated information. Specifically, we use the tools of information geometry to better understand the `structure' of information in physical systems. �Improve on statistical and computational techniques for estimating integrated information. Current methods limit applications to small systems, but better tools will open up many exciting possible applications. �Demonstrate the value of integrated information in applications. Applications will be primarily based in neuroscience, but also exploring opportunities in other disciplines. My research is highly interdisciplinary and collaborative, and will have a direct impact on several fields within the natural sciences and engineering. The expected results will provide a new (geometric) perspective on the structure of integrated information. The results will also establish a definition of integrated information for continuous variables, exposing the existing methods of integrated information to new domains of application. Accurate estimates of integrated information will provide new opportunities to analyse larger models. While originally developed by considering the brain as a system of interacting neurons, integrated information has found applications in other natural science fields, from biology to physics. Validated techniques for estimating integrated information will stimulate the application of causal and graphical models in these fields.