Seminar "Towards a quantitative understanding of high-order interdependencies in complex systems"


2021年5月24日 (月) 16:30 17:30





Name: Fernando E. Rosas

Affiliation: Data Science Institute, Imperial College London



While the notions of complexity, synergy, and emergence suggest promising avenues to tackle problems such as the mind-body relationship, they have been as much a cause of wonder as a perennial source of philosophical headaches. Part of the difficulty in deepening our understanding on these subjects lies in the absence of simple analytical models and clear metrics that could serve the community to guide discussions and mature theories. In this talk we present practically useful approaches to capture complex and emergent phenomena in multivariate systems, and discuss their applicability in scenarios of interest.

The first part of the talk will be devoted to the notion of brain entropy and Lempel-Ziv complexity (LZ).
We will first provide a brief introduction of what LZ measures, how it works, and how it relates with the Entropic Brain Hypothesis. Then we will review existing results of LZ as a method to assess level of consciousness across a wide range of conditions, review some current limitations of standard implementations of LZ, and explore novel approaches to move beyond them.

The second part of the talk will be focused on integrated information and emergence. We will review the origins of integrated information theory, and explore recent renditions under the framework of Integrated Information Decomposition (ΦID). We will show how ΦID allows us to better understand well-known metrics of dynamical complexity such as integrated information, and suggest refinements that appear to have improved practical effectiveness. 

Additionally, ΦID lets us capture high-order dynamical phenomena that have not been reported in the literature, which can be used as a foundation for a theory of causal emergence - that in turn can be used to operationalise key principles from the Global Neuronal Workspace Theory.

We conclude discussing how these ideas and tools enable new ways for studying high brain function, which lay in a middle ground between computational and dynamical system approaches.

Zoom link


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