July 5-9, 2025
Florence, Italy
The following are invited and contributing speakers for the workshop.
We would like to call for contributions of talks in addition to our invited speakers. If you are interested in contributing such a talk, please send a title and abstract to Marilyn Gatica (marilyn.gatica@nulondon.ac.uk) or Abdullah Makkeh (abdullah.alimakkeh@uni-goettingen.de).
Earlier submissions and those from female/minority speakers will be prioritised. Submitted talks will range in length from 20 to 45 minutes (including Q&A); please indicate in your submission if you prefer to be considered for a short talk only.
tba
Giovanni Petri - tba
Marilyn Gatica - "High-order functional interactions in health and therapy"
In this talk, I will discuss the relevance of understanding high-order interactions in clinical applications using fMRI data, highlighting their significance in healthy aging in humans and their potential in therapeutic applications, such as transcranial ultrasound stimulation, in both human and macaque brains.
Luca Faes - "Measuring Directed and Undirected High-Order Interactions in Dynamic Physiological Networks through Information Rate Decomposition"
Information Decomposition tools are emerging as principled and flexible frameworks to unveil complex high-order interactions
in a wide range of network systems in computational neuroscience and physiology. Two of the most popular tools
in this context are the O-information (OI), an undirected metric quantifying the balance between redundant and
synergistic high-order interactions among three or more random variables, and the partial information decomposition
(PID), a directed approach capable of dissecting the information shared between an assigned target variable and two
or more sources into separate redundant and synergistic contributions. In spite of being defined specifically for
random variables, these tools are ubiquitously applied to multivariate neurophysiological time series interpreted as
realizations of random processes with temporal statistical structure. In this work, to overcome the incorrect depiction of
high-order effects by OI and PID when applied to dynamic networks, we introduce the frameworks of O-information rate (OIR)
and partial information rate decomposition (PIRD). By leveraging mutual information rate (MIR) instead of mutual information
(MI), our approach decomposes the dynamic information shared by multivariate random processes into unique, redundant,
and synergistic contributions obtained aggregating information rate atoms in a principled manner.
To solve PIRD, we define a pointwise redundancy rate function based on the minimum MI principle applied locally in the
frequency-domain representation of the processes. The framework is validated in benchmark simulations of dynamic Gaussian
systems, demonstrating its advantages over traditional OI and PID schemes and showing how the spectral representation may
reveal scale-specific higher-order interactions that are obscured in the time domain. OIR and PIRD are then applied to exemplary
physiological network systems with dynamic behavior probed measuring multivariate neurophysiological time series.
Maria Pope - tba
This workshop has been run at CNS for over two decades now -- links to the websites for the previous workshops in this series are below:
Image modified from an original credited to dow_at_uoregon.edu, obtained here (distributed without restrictions); modified image available here under CC-BY-3.0