Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning techniques, next-generation …

Brain-wide fluctuations in local field potential oscillations reflect emergent network-level
signals that mediate behavior. Cracking the code whereby these oscillations coordinate in …

When governed by underlying low-dimensional dynamics, the interdependence of
simultaneously recorded populations of neurons can be explained by a small number of …

We present a novel extension of multi-output Gaussian processes for handling
heterogeneous outputs. We assume that each output has its own likelihood function and use …

Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear
combinations of independent, latent, single-output Gaussian processes (GPs). This resulted …

The architecture whereby activity across many brain regions integrates to encode individual
appetitive social behavior remains unknown. Here we measure electrical activity from eight …

Modern recording techniques now allow us to record from distinct neuronal populations in
different brain networks. However, especially as we consider multiple (more than two) …

We present MOGPTK, a Python package for multi-channel data modelling using Gaussian
processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible …

We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP)
datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels …

We propose the Wasserstein-Fourier (WF) distance to measure the (dis) similarity between
time series by quantifying the displacement of their energy across frequencies. The WF …