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UMAP: Uniform Manifold Approximation and Projection
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.
Bayesian Context Aggregation for Neural Processes
TLDR
This paper demonstrates on a range of challenging experiments that BA consistently improves upon the performance of traditional mean aggregation while remaining computationally efficient and fully compatible with existing NP-based models.
Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure
TLDR
In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns and handles efficiently the additional information from increasingly large neuronal ensembles.
A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles
TLDR
In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDistClust can reconstruct cortical response properties in an unsupervised function, that is, without knowledge of stimulus identity or delivery times.
Investigating Music imagery as a Cognitive Paradigm for low-Cost brain-Computer Interfaces
TLDR
A novel paradigm that relies on music imagery and mental subtraction is devised that can be reliably executed, without the need for subject training and can be used to develop accessible BCIs for patients in the future.
Uncertainty through Sampling: The Correspondence of Monte Carlo Dropout and Spiking in Artificial Neural Networks
TLDR
It is demonstrated that in cases of incomplete world knowledge (epistemic uncertainty) as well as for noisy observations (aleatoric uncertainty) both neuron models show similar uncertainty representations, providing evidence that sampling could play a fundamental role in representing uncertainties in neural systems.
Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure
TLDR
An unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), is introduced, for their detection from high-dimensional neural ensembles, and proposes a dissimilarity measure between neuronal patterns based on optimal transport theory.