Synergy and Redundancy Measures: Theory and Applications to Characterize Complex Systems, Build Models, and Shape Neural Network Representations
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 20 September 2026 | Viewed by 941
Special Issue Editor
Interests: data analysis; causal inference; dimensionality reduction; neuroscience; sensitivity analysis; structure learning; information decomposition; information bottlenecks; fairness
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
An important aspect of how sources of information are distributed across a set of variables concerns whether different variables provide redundant, unique, or synergistic information when combined with other variables. Intuitively, variables share redundant information if each variable individually carries the same information carried by other variables. Information carried by a certain variable is unique if it is not carried by any other variables or their combination, and a group of variables carries synergistic information if some information arises only when they are combined.
Recent advances have contributed to build an information-theoretic framework to determine the distribution and nature of information extractable from multivariate datasets. Measures of redundant, unique, or synergistic information characterize dependencies between the parts of a multivariate system and can help to understand its function and mechanisms. Furthermore, these measures are also useful to analyze how information is distributed across layers in neural networks or can be used as cost functions to shape the structure of data representations learned by the networks.
This Special Issue welcomes submissions that contribute to advances both in theoretical formulations and in the applications of information-theoretic measures of synergy and redundancy. This Special Issue encompasses the following:
- Advances in a multivariate formulation of redundancy measures or in the comparison of alternative proposals, addressing their distinctive power to capture relevant structures in both synthetic and experimental datasets.
- Applications to understand interactions in real complex systems.
- Advances in the estimation of information-theoretic quantities from high-dimensional datasets.
- Applications for feature selection and sensitivity analysis.
- Analyses of the distribution and nature of information across layers in neural networks.
- Designs of deep learning models to obtain robust or disentangled data representations.
- Fairness analyses.
Dr. Daniel Chicharro
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- mutual information
- synergy
- redundancy
- unique information
- neural networks
- disentanglement
- feature extraction
- representation learning
- partial information decomposition
- fairness
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