130 citations · 241 across the 8 of their papers we have counts for
9 papers
An information-geometric formulation of distributed computation
Alberto Liardi, George Blackburne, Pedro A. M. Mediano
Understanding how complex systems perform computations, i.e. how they store, transmit, and transform information across their components, is a major goal across the natural science…
Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Geometry in Human and Machine Vision
Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin
To humans, a robin seems more like a bird than a bird seems like a robin, but does this asymmetry also hold for machine vision? Humans and modern vision models can match each other…
Integrated information theory: the good, the bad and the misunderstood
Adam B. Barrett, Borjan Milinkovic, Pedro A. M. Mediano +4
The integrated information theory of consciousness (IIT) is uniquely ambitious in proposing a mathematical formula, derived from apparently fundamental properties of conscious expe…
From Data Statistics to Feature Geometry: How Correlations Shape Superposition
Lucas Prieto, Edward Stevinson, Melih Barsbey +2
A central idea in mechanistic interpretability is that neural networks represent more features than they have dimensions, arranging them in superposition to form an over-complete b…
The mathematical landscape of partial information decomposition: A comprehensive review of properties and measures
Alberto Liardi, Keenan J. A. Down, George Blackburne +2
Partial Information Decomposition (PID) has become one of the most prominent information-theoretic frameworks for describing the structure and quality of information in complex sys…
Same Compression Principle, Different Geometry: Rate-Distortion Signatures Dissociate Biological and Artificial Visual Systems
Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin
Efficient coding theory predicts that biological perceptual systems compress sensory input optimally under resource constraints, with the systematic structure of errors reflecting…
Shannon invariants: A scalable approach to information decomposition
Aaron J. Gutknecht, Fernando E. Rosas, David A. Ehrlich +3
Distributed systems, such as biological and artificial neural networks, process information via complex interactions engaging multiple subsystems, resulting in high-order patterns…
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
Leonardo Novelli, Patricia Wollstadt, Pedro Mediano +2
Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free m…
IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
Patricia Wollstadt, Joseph T. Lizier, Raul Vicente +5
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using…