This talk explores the emerging field of multivariate information theory and the phenomenon of higher-order, synergistic interactions in complex systems: dependencies between three or more elements that are irreducible to the sum of their parts. In it, I make an analogy to the idea of “dark matter” in cosmology: dark matter forms the majority of the apparent mass of the Universe, but our understanding of its nature is limited by our inability to detect or interact with it. Similarly, “dark information”, being composed of the combinatorially vast space of possible polyadic interactions in a complex system, may form the vast majority of the computational “structure” of a system - but existing statistical tools are almost completely blind to these interactions. Using computational neuroscience as a case-study, I show how two foundational techniques in modern neuroscience (correlation networks and manifold learning algorithms) are insensitive to higher-order synergies, and instead only provide a narrow window into a small slice of the overall structure brain activity. This large space of higher-order interactions in the brain has therefore gone almost completely unexplored, and may contain novel insights into how the interaction of large numbers of components gives rise to emergent features of cognition and consciousness. Finally, I end by arguing that “dark information” is almost certainly not unique to the brain, but instead is a feature of any complex system.
About the speaker
About the speaker
Thomas Varley is a complex systems scientist and information theorist currently working as a postdoc in the Vermont Complex Systems Institute at the University of Vermont. Thomas’ research interests are broad, having published in diverse fields ranging from multivariate information theory, to the neurobiology of consciousness-altering drugs, to the structure of synthetic biobots called xenobots. The common thread of all their research projects is the quantitative study of how multipartite interactions in complex systems can produce “emergent” properties that are irreducible to the sum of the individual parts, and the implications of these higher-order interactions in biology, health, and disease. Thomas earned a dual PhD from Indiana University Bloomington, double-majoring in complex systems science and computational neuroscience. While at Indiana University, Thomas was a proud member of Indiana Graduate Workers Union and a union representative to the Department of Psychological and Brain Sciences. Previously, he studied at the University of Cambridge, earning an MPhil degree in clinical neuroscience and a BA from Hampshire College. When not doing science, he writes fiction and creative non-fiction, and enjoys both glassblowing and wheel-throwing ceramics.
Thomas Varley is a complex systems scientist and information theorist currently working as a postdoc in the Vermont Complex Systems Institute at the University of Vermont. Thomas’ research interests are broad, having published in diverse fields ranging from multivariate information theory, to the neurobiology of consciousness-altering drugs, to the structure of synthetic biobots called xenobots. The common thread of all their research projects is the quantitative study of how multipartite interactions in complex systems can produce “emergent” properties that are irreducible to the sum of the individual parts, and the implications of these higher-order interactions in biology, health, and disease. Thomas earned a dual PhD from Indiana University Bloomington, double-majoring in complex systems science and computational neuroscience. While at Indiana University, Thomas was a proud member of Indiana Graduate Workers Union and a union representative to the Department of Psychological and Brain Sciences. Previously, he studied at the University of Cambridge, earning an MPhil degree in clinical neuroscience and a BA from Hampshire College. When not doing science, he writes fiction and creative non-fiction, and enjoys both glassblowing and wheel-throwing ceramics.
