Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Aobo Lyu, Andrew Clark, Netanel Raviv
While mutual information effectively quantifies dependence between two variables, it cannot capture complex, fine-grained interactions that emerge in multivariate systems. The Partial Information Decomposition (PID) framework was introduced to address this by decomposing the mutual information between a set of source…
Chao Tian, Shlomo Shamai (Shitz), Mario Martinelli, Kai Niu + 2 more
'Meixia Tao' 'Youlong Wu'] Partial information decomposition has recently found applications in biological signal processing and machine learning. Despite its impacts, the decomposition was introduced through an informal and heuristic route, and its exact operational meaning is unclear. In this work, we fill this gap…
Thomas F. Varley, Patrick Kaminski, Geert Verdoolaege
The theory of intersectionality proposes that an individual’s experience of society has aspects that are irreducible to the sum of one’s various identities considered individually, but are “greater than the sum of their parts”. In recent years, this framework has become a frequent topic of discussion both in social…
Conor Finn, Joseph T. Lizier
What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice…
Artemy Kolchinsky
Consider a situation in which a set of n "source" random variables X 1, . . . , X n have information about some "target" random variable Y . For example, in neuroscience Y might represent the state of an external stimulus and X 1, . . . , X n the activity of n different brain regions. Recent work in information theory…
Kyle Schick-Poland, Abdullah Makkeh, Aaron J. Gutknecht, Patricia Wollstadt + 2 more
'Patricia Wollstadt' 'Anja Sturm' 'Michael Wibral'] Conceptually, partial information decomposition (PID) is concerned with separating the information contributions several sources hold about a certain target by decomposing the corresponding joint mutual information into contributions such as synergistic, redundant, or…
Aobo Lyu, Andrew E. Clark, Netanel Raviv
—Mutual information between two random variables is a well-studied notion, whose understanding is fairly complete. Mutual information between one random variable and a pair of other random variables, however, is a far more involved notion. Specifically, Shannon's mutual information does not capture finegrained…
Tobias Mages, Christian Rohner, Daniel Chicharro
The idea of a partial information decomposition (PID) gained significant attention for attributing the components of mutual information from multiple variables about a target to being unique, redundant/shared or synergetic. Since the original measure for this analysis was criticized, several alternatives have been…
Nigel Colenbier, Frederik Van de Steen, Lucina Q. Uddin, Russell A. Poldrack + 2 more
In resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It…
Andrea I. Luppi, Pedro A.M. Mediano, Fernando E. Rosas, Judith Allanson + 10 more
A central goal of neuroscience is to understand how the brain orchestrates information from multiple input streams into a unified conscious experience. Here, we address two fundamental questions: how is the human information-processing architecture functionally organised, and how does its organisation support…
Conor Finn, Joseph T. Lizier
What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice…
Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield
The partial information decomposition (PID) is a promising framework for decomposing a joint random variable into the amount of influence each source variable $X_{i}$ has on a target variable Y, relative to the other sources. For two sources, influence breaks down into the information that both $X_{0}$ and $X_{1}$…
Aaron J. Gutknecht, Michael Wibral, Abdullah Makkeh
Partial information decomposition (PID) seeks to decompose the multivariate mutual information that a set of source variables contains about a target variable into basic pieces, the so called "atoms of information". Each atom describes a distinct way in which the sources may contain information about the target. For…
Tobias Mages, Elli Anastasiadi, Christian Rohner, Daniel Chicharro
Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable…
Edoardo Pinzuti, Patricia Wollsdtadt, Aaron Gutknecht, Oliver Tüscher + 1 more
Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to…
Jan Bím, Vito De Feo, Daniel Chicharro, Malte Bieler + 3 more
Quantifying both the amount and content of the information transferred between neuronal populations is crucial to understand brain functions. Traditional data-driven methods based on Wiener-Granger causality quantify information transferred between neuronal signals, but do not reveal whether transmission of information…
Thomas F. Varley, Patricia Wollstadt
Since its introduction, the partial information decomposition (PID) has emerged as a powerful, information-theoretic technique useful for studying the structure of (potentially higher-order) interactions in complex systems. Despite its utility, the applicability of the PID is restricted by the need to assign elements…
Hinata Nago, Hiroki Kojima, Hiroyuki Yamaguchi, Yuichi Yamashita
Deficits in neural information integration are hypothesized to underlie diverse psychiatric symptoms, yet the specific patterns of alteration across different disorders remain unclear. In this study, we decomposed information dynamics between brain regions into synergistic and redundant components using a recent…
Ahmet Altun, Isaac Francois Leach, Frank Neese, Giovanni Bistoni
We introduce the fragment-pairwise Local Energy Decomposition (fp-LED) scheme for precise quantification of individual interactions contributing to the binding energy of arbitrary chemical entities, such as protein-ligand binding energies, lattice energies of molecular crystals, or association energies of large…
Authors not listed
Quantum state tomography has been widely used to reconstruct the quantum state of a system from a set of informationally-complete measurements. Obtaining enough information about, e.g., the wavefunction of a molecule allows its complete characterization. On the other hand, deep learning models for molecular property…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…