Lecturer: Matteo Neri & Mar Estarellas
Fields: Complexity, Information theory, neuroscience
Content
Within complexity sciences, various approaches have been developed to investigate the intricate web of interactions underpinning the behavior of complex systems, such as society or the brain. In the last two decades, a great deal of success was reached by theoretical and computational frameworks based on the study of pairwise interactions [1]. Recently, a growing body of literature focused on the extension of these approaches to the study of higher-order interactions, i.e. between more than 2 units of a system [2, 3]. In this course, we will introduce some of the main concepts in the field (e.g. synergy and redundancy in terms of information content [4]), discuss results obtained by previous work, and show how concretely they can be employed in the study of real-world data, with a special focus on cognitive neuroscience and consciousness [5].
We will present our work, focusing on the relationship between the human brain and its internal processes as well as its interaction with the world around us, adopting information-theoretic approaches to analyze neuroimaging data collected with different techniques, in particular MEG, EEG, and fMRI.
Additionally, we aim to offer a hands-on experience by providing access to a cutting-edge toolbox [6], making the session interactive and applicable to real-world scenarios.
The proposed syllabus will be the following:
– Basic introduction to graph and information theoretical approaches, focusing on higher-order interactions.
– State of the art in using these measures to investigate complexity from brain to economy, with a special focus on cognition and consciousness science.
– Hands-on experience (bring your computer if you can!)
Literature
- Barabasi (2012) The network takeover https://www.nature.com/articles/nphys2188
- Varley (2023) Information Theory for Complex Systems Scientists: What, Why and How https://arxiv.org/abs/2304.12482
- Battiston, et al. (2021) The physics of higher-order interactions in complex systems https://www.nature.com/articles/s41567-021-01371-4
- Williams and Beer, 2010 Nonnegative Decomposition of Multivariate Information https://arxiv.org/abs/1004.2515
- Luppi, et al (2024) Information decomposition and the informational architecture of the brain https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(23)00284-X
- 6. The toolbox HOI (part of an ongoing project) https://brainets.github.io/hoi/overview/ovw_theory.html
Lecturer
Matteo Neri is a PhD student in Cognitive Neuroscience, at Aix-Marseille Université with a background in Mathematics and Complex Systems Physics. His main interests are the study of complexity and emergence in the investigation of cognitive neuroscience and psychology, addressing questions as: how cognitive functions and consciousness relates to brain activity? What can we understand studying the activity patterns of different units of a complex system?
Affiliation: Institut de Neuroscience de la Timone
Homepage: https://www.int.univ-amu.fr/
Mar Estarellas is a postdoctoral researcher at the Consciousness and Cognition Lab, Cambridge University. The objective of her current post-doctoral research project is twofold. First, to employ a diverse range of analytical and computational methods, with an emphasis on information theory, to discern the neural informational features that correlate with consciousness and cognition across a spectrum of conscious states and populations, including those affected by dementia. Second, beyond the pursuit of fundamental scientific knowledge, a significant portion of this research is dedicated to using these biomarkers for early detection, stratification, and prognosis of dementia.She is deeply passionate about the symbiotic relationship between art, nature, and contemplative practices, and their collective potential to foster mental and ecological well-being.
Affiliation: Consciousness and Cognition Lab, Queen Mary University of London & University of Cambridge, UK