The current formulation of wavelets is confusing and opaque. This publication recommends a different formula for defining wavelets based on the amount of temporal-spectral smoothing in milliseconds or Hertz.
Participants will learn about neural time series analysis, synchronization, statistics, multivariate source separation, and more.
Covariance-based dimension-reduction and source-separation methods for multivariate data. Most examples use simulated and real EEG data, but the methods are valid for any multichannel time series data.
Brief descriptions
”Response conflict” is the feeling you have when you are about to walk into another person and don’t know whether you should go left or right. We study the unique signature in human EEG of response conflict, which is termed ”midfrontal theta.”
What neural circuit mechanisms produce midfrontal theta and conflict computations? This is an important question, yet has no good answer. We use high-density multisite and multiarea electrophysiology in combination with optogenetics and electrical microstimulation (in rodents) to discover circuit mechanisms of frontal theta. These findings are integrated with biophysical computational models.
We use large-scale electrophysiology and optogenetics in Th:Cre rats to understand how optically indiced phasic up/down-regulation of dopamine regulates synchronization in PFC-striatal-VTA circuits.
Neuroscience is awash with data analysis methods, yet many methods lack rigorous testing of parameter ranges and violations of key assumptions in empirical data. We simulate EEG and LFP data to evaluate existing, and develop new, cutting-edge analysis methods. Simulations are useful because they maximize control over signal and noise characteristics.
20 hours of clear explanations of concepts linear algebra, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular value decomposition. The course has a strong focus on modern applications-oriented aspects of linear algebra. MATLAB and Python codes are included.
Learn the theoretical and computational bases of the Fourier transform, and its implementation in modern applications in digital signal processing, data analysis, and image filtering. The course covers the basics and also advanced topics including effects of non-stationarities, spectral resolution, normalization, filtering. MATLAB and Python codes are included.
