Talks by Giovanni Petri & Daniele Marinazzo.
We are pleased to invite you to a mini-workshop organized as a scientific activity of the Computational Neuroscience Center (CONECT) of the INT focused on “Higher-order interactions in brain networks: from theory to data analysis”.
Two speakers will present their work from two complementary theoretical perspectives, emerged from recent advances in network science (Giovanni Petri) and information theory (Daniele Marinazzo).
If you are interested in the topic and/or want to learn something new, please join us in the salle Laurent Vinay on Wednesday 5th October 2022 at 14:30.
Here are the titles and abstracts of the two presentations:
Title: Between higher-order mechanisms and phenomena
Abstract: Complex networks have become the main paradigm for modelling the dynamics of complex interacting systems. However, networks are intrinsically limited to describing pairwise interactions, whereas real-world systems are often characterized by higher-order interactions involving groups of three or more units. Higher-order structures, such as hypergraphs and simplicial complexes, are therefore a better tool to map the real organization of many social, biological and man-made systems. At the same time, higher-order observables, typically topological or information-theoretic in nature and often sharing the same simplicial language, have been gathering attention for their capacity to capture properties of complex systems that are invisible to standard statistical descriptions. This had led to a certain confusion between these two facets, mechanisms on one side, phenomena on the other. Here, using recent examples from both computational modeling and neuroimaging analysis, I highlight collective behaviours induced by higher-order interactions, their interface with recent advances in topological data analysis, and finally outline three key challenges for the physics of higher-order complex systems.
Title: Two is company, three is a party, and how not to get lost in big parties: practical considerations on higher-order data analysis.
Abstract: I will make the case for looking for high-order interactions in (neural) data, and for trying to do so in a principled yet feasible way (who needs yet another axiom?). I will then loop back and show how we can find low-order descriptors of high-order interactions (we’re not good in figuring things in high dimensions).