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UMAP: Uniform Manifold Approximation and Projection
TLDR
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. Expand
Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure
TLDR
We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. Expand
Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure
TLDR
We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. Expand
Investigating Music imagery as a Cognitive Paradigm for low-Cost brain-Computer Interfaces
TLDR
We introduce an easy-to-use two-class paradigm that relies on music imagery and mental subtraction for BCI control without the need for motor-abilities. Expand
Uncertainty through Sampling: The Correspondence of Monte Carlo Dropout and Spiking in Artificial Neural Networks
TLDR
We empirically investigate the correspondence of uncertainty representations in spiking network models and Monte Carlo dropout in rate-based neural networks. Expand
A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles
TLDR
We introduce an unsupervised method, SPOTDistClust (Spike Pattern Optimal Transport Distance Clustering), for their detection from high-dimensional neural ensembles. Expand