Search Results for author:
Found 7 papers, 1 papers with code
Analysis of Brain States from Multi-Region LFP Time-Series
The LFPs are modeled as a mixture of GPs, with state- and region-dependent mixture weights, and with the spectral content of the data encoded in GP spectral mixture covariance kernels.
On the relations of LFPs & Neural Spike Trains
One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes.
GP Kernels for Cross-Spectrum Analysis
An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels.
Cross-Spectral Factor Analysis
To facilitate understanding of network-level synchronization between brain regions, we introduce a novel model of multisite low-frequency neural recordings, such as local field potentials (LFPs) and electroencephalograms (EEGs).
Targeting EEG/LFP Synchrony with Neural Nets
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 required to train complex models (e. g., conventional deep learning methods).
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
Targeted stimulation of the brain has the potential to treat mental illnesses.
Directed Spectrum Measures Improve Latent Network Models Of Neural Populations
We prove that it is compatible with the implicit assumptions of linear factor models, and we provide a method to estimate the DS.
