498 citations · 1.6k across the 46 of their papers we have counts for
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Null models for comparing information decomposition across complex systems
Alberto Liardi, Fernando E. Rosas, Robin L. Carhart-Harris +3
A key feature of information theory is its universality, as it can be applied to study a broad variety of complex systems. However, many information-theoretic measures can vary sig…
Characterising high-order interdependence via entropic conjugation
Fernando E. Rosas, Aaron Gutknecht, Pedro A. M. Mediano +1
High-order phenomena play crucial roles in many systems of interest, but their analysis is often highly nontrivial. There is a rich literature providing a number of alternative inf…
The Fast Möbius Transform: An algebraic approach to information decomposition
Abel Jansma, Pedro A. M. Mediano, Fernando E. Rosas
The partial information decomposition (PID) and its extension integrated information decomposition (ID) are promising frameworks to investigate information phenomena involving m…
Algebraic Representations of Entropy and Fixed-Parity Information Quantities
Keenan J. A. Down, Pedro A. M. Mediano
Many information-theoretic quantities have corresponding representations in terms of sets. The prevailing signed measure space for characterising entropy, the -measure of Yeung,…
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
Clémentine C. J. Dominé, Nicolas Anguita, Alexandra M. Proca +4
Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models reli…
A Logarithmic Decomposition and a Signed Measure Space for Entropy
Keenan J. A. Down, Pedro A. M. Mediano
The Shannon entropy of a random variable has much behaviour analogous to a signed measure. Previous work has explored this connection by defining a signed measure on abstract sets,…
Evolving reservoir computers reveals bidirectional coupling between predictive power and emergent dynamics
Hanna M. Tolle, Andrea I Luppi, Anil K. Seth +1
Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional ap…
Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
Carles Balsells-Rodas, Yixin Wang, Pedro A. M. Mediano +1
Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assu…
Characterising directed and undirected metrics of high-order interdependence
Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar
Systems of interest for theoretical or experimental work often exhibit high-order interactions, corresponding to statistical interdependencies in groups of variables that cannot be…
Software in the natural world: A computational approach to hierarchical emergence
Fernando E. Rosas, Bernhard C. Geiger, Andrea I Luppi +4
Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent adv…