Complexity Science Group

Founded in 2005, the Complexity Science Group is the first transdisciplinary group in complexity science in Canada. We tackle problems of structure, organization, and dynamics in diverse settings ranging from living systems and the brain to material science and the Earth system as well as social dynamics. Particular examples include spreading and triggering processes, seismicity and fracture phenomena as well as cardiac arrhythmia and, more fundamentally, nonlinear chemical reaction kinetics (Davidsen). The latter is directly related to the work of the 2007 Nobel laureate in chemistry, physicist Gerhardt Ertl. Other examples are protein interaction networks, niche development in microbial communities, disease and rumor spreading, and dynamics of innovations (Paczuski). More recently, we have also focused on neuronal systems and the brain, including network neuroscience, network control, biological learning and more broadly computational neuroscience (Davidsen, Nicola, Orlandi, Towlson), taking in particular a leadership role in the newly established University of Calgary's Computational Neuroscience Platform and actively participating in the International Network for Bio-Inspired Computing. Related to this, we have also started to tackle complex condensed matter and materials science problems at multiple scales, often related to neuromorphic materials (Da Rocha): At the macro-scale, we investigate physical properties of cognitive network materials such as spaghetti-like nanowire networks with memristive characteristics that can be used in future brain-inspired technologies. At the micro-scale, we are interested at quantum phenomena taking place in low-dimensional systems such as atomic clusters, nanotubes, nanowires, and 2D materials. As a group, we particularly look for avenues to "see across" different fields of inquiry to discover common principles that lead to nontrivial predictions.

One of our main interests is in empirically observable phenomena where heterogeneous structures emerge at macroscopic scales due to constituent microscopic entities and their underlying dynamics. We develop ways to characterize, classify and model such complex systems, and the procedures by which they are measured, using an empirically driven methodology that takes advantage of breakthroughs in high precision measurements and experiments as well as information technology. We use a broad range of tools from different parts of mathematical science to tackle the "complexity wall". These include information and communication theories, stochastic processes, nonlinear dynamics, statistical physics, probabilistic inference, and modern network theory.

Information on the 2018 "International Symposium on Complexity Science Approaches to Brain Dynamics" can be found here.

Information on the 2019 "International Workshop on Function, Information Spreading, and Percolation in Brain Networks" can be found here.

Information on the 2022 (postponed from 2020) "International Symposium and Workshop on Multiplex Brain Networks" can be found here.