output
20142021
most citedUntangling cross-frequency coupling in neuroscience

651 citations

5 papers

cs.LG2021

Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data

Katharine A. Shapcott, Alex D. Bird

We introduce a novel type of computationally efficient artificial neural network (ANN) called the rank similarity filter (RSF). RSFs can be used to both transform and classify nonl…

stat.ME20211 cited

What to do if N is two?

Pascal Fries, Eric Maris

The field of in-vivo neurophysiology currently uses statistical standards that are based on tradition rather than formal analysis. Typically, data from two (or few) animals are poo…

cs.HC202015 cited

Toward Agile Situated Visualization: An Exploratory User Study

Leonel Merino, Boris Sotomayor-Gómez, Xingyao Yu +4

We introduce AVAR, a prototypical implementation of an agile situated visualization (SV) toolkit targeting liveness, integration, and expressiveness. We report on results of an exp…

q-bio.NC2014651 cited

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…

q-bio.NC2014151 cited

Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data

Sol Lim, Cheol E. Han, Peter J. Uhlhaas +1

Human brain maturation is characterized by the prolonged development of structural and functional properties of large-scale networks that extends into adulthood. However, it is not…