activity
20182022
most citedA partial information decomposition for discrete and continuous variables

9 citations · 13 across the 2 of their papers we have counts for

collaborators

8 papers

q-bio.NC20224 cited

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…

cs.IT20219 cited

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…

q-bio.PE2020

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…

cs.IT2020

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…

cs.ET2019

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…

q-bio.NC2019

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…