9 citations · 22 across the 8 of their papers we have counts for
10 papers · 1 filter
Novel Inconsistency Results for Partial Information Decomposition
Philip Hendrik Matthias, Abdullah Makkeh, Michael Wibral +1
Partial Information Decomposition (PID) seeks to disentangle how information about a target variable is distributed across multiple sources, separating redundant, unique, and syner…
Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks
Mark Blümel, Andreas C. Schneider, Valentin Neuhaus +5
Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the local computational princip…
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…
What should a neuron aim for? Designing local objective functions based on information theory
Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich +4
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological system…
Partial Information Decomposition for Continuous Variables based on Shared Exclusions: Analytical Formulation and Estimation
David A. Ehrlich, Kyle Schick-Poland, Abdullah Makkeh +3
Describing statistical dependencies is foundational to empirical scientific research. For uncovering intricate and possibly non-linear dependencies between a single target variable…
A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions
Abdullah Makkeh, Marcel Graetz, Andreas C. Schneider +3
Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions…