943 citations · 3k across the 23 of their papers we have counts for
23 papers
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…
From Babel to Boole: The Logical Organization of Information Decompositions
Aaron J. Gutknecht, Abdullah Makkeh, Michael Wibral
The conventional approach to the general Partial Information Decomposition (PID) problem has been redundancy-based: specifying a measure of redundant information between collection…
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition
David A. Ehrlich, Andreas C. Schneider, Viola Priesemann +2
In neural networks, task-relevant information is represented jointly by groups of neurons. However, the specific way in which this mutual information about the classification label…
Intrinsic timescales of spiking activity in humans during wakefulness and sleep
Annika Hagemann, Marcel Stephan Kehl, Jonas Dehning +5
Information processing in the brain requires integration of information over time. Such an integration can be achieved if signals are maintained in the network activity for the req…
Dendritic predictive coding: A theory of cortical computation with spiking neurons
Fabian A. Mikulasch, Lucas Rudelt, Michael Wibral +1
Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experi…
Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies
Patricia Wollstadt, Daniel L. Rathbun, W. Martin Usrey and +4
Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in…
A partial information decomposition for discrete and continuous variables
Kyle Schick-Poland, Abdullah Makkeh, Aaron J. Gutknecht +3
Conceptually, partial information decomposition (PID) is concerned with separating the information contributions several sources hold about a certain target by decomposing the corr…
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral
Selecting a minimal feature set that is maximally informative about a target variable is a central task in machine learning and statistics. Information theory provides a powerful f…
The challenges of containing SARS-CoV-2 via test-trace-and-isolate
Sebastian Contreras, Jonas Dehning, Matthias Loidolt +7
Without a cure, vaccine, or proven long-term immunity against SARS-CoV-2, test-trace-and-isolate (TTI) strategies present a promising tool to contain its spread. For any TTI strate…
Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic
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, th…
Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions
Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner +4
As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A main ch…
Introducing a differentiable measure of pointwise shared information
Abdullah Makkeh, Aaron J. Gutknecht, Michael Wibral
Partial information decomposition (PID) of the multivariate mutual information describes the distinct ways in which a set of source variables contains information about a target va…
Control of criticality and computation in spiking neuromorphic networks with plasticity
Benjamin Cramer, David Stöckel, Markus Kreft +4
The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challe…
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…
Dynamic Adaptive Computation: Tuning network states to task requirements
Jens Wilting, Jonas Dehning, Joao Pinheiro Neto +4
Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid an…
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…
Breakdown of local information processing may underlie isoflurane anesthesia effects
Patricia Wollstadt, Kristin K. Sellers, Lucas Rudelt +4
The disruption of coupling between brain areas has been suggested as the mechanism underlying loss of consciousness in anesthesia. This hypothesis has been tested previously by mea…
Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions
Michael Wibral, Viola Priesemann, Jim W. Kay +2
In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is th…
A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions
Patricia Wollstadt, Ulrich Meyer, Michael Wibral
Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to re…
Bits from Biology for Computational Intelligence
Michael Wibral, Joseph T. Lizier, Viola Priesemann
Computational intelligence is broadly defined as biologically-inspired computing. Usually, inspiration is drawn from neural systems. This article shows how to analyze neural system…
Untangling cross-frequency coupling in neuroscience
Juhan Aru, Jaan Aru, Viola Priesemann +5
Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pat…
Efficient transfer entropy analysis of non-stationary neural time series
Patricia Wollstadt, Mario Martínez-Zarzuela, Raul Vicente +2
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of informati…