154 citations · 216 across the 11 of their papers we have counts for
11 papers
Inferring Mechanisms from Bits: Promises and Pitfalls of Higher-Order Information in Cognitive Science
D. Rebbin, K. J. A. Down, T. F. Varley +2
Higher-order information theory has become a rapidly growing toolkit in computational neuroscience, motivated by the idea that multivariate dependencies can reveal aspects of neura…
Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors
Y. Duan, J. Zhan, J. Gross +2
To interpret our surroundings, the brain uses a visual categorization process. Current theories and models suggest that this process comprises a hierarchy of different computations…
A whitening approach for Transfer Entropy permits the application to narrow-band signals
Christoph Daube, Joachim Gross, Robin A. A. Ince
Transfer Entropy, a generalisation of Granger Causality, promises to measure "information transfer" from a source to a target signal by ignoring self-predictability of a target sig…
Deeper Interpretability of Deep Networks
Tian Xu, Jiayu Zhan, Oliver G. B. Garrod +4
Deep Convolutional Neural Networks (CNNs) have been one of the most influential recent developments in computer vision, particularly for categorization. There is an increasing dema…
Exact partial information decompositions for Gaussian systems based on dependency constraints
James W. Kay, Robin A. A. Ince
The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new…
The Partial Entropy Decomposition: Decomposing multivariate entropy and mutual information via pointwise common surprisal
Robin A. A. Ince
Obtaining meaningful quantitative descriptions of the statistical dependence within multivariate systems is a difficult open problem. Recently, the Partial Information Decompositio…