Covers methods that are useful for analyzing neural data including conventional statistics, mutual information, point process models and decoding analyses. Emphasis is on explaining the basic mathematical intuitions behind these methods, and giving practical hands-on experience for how these methods can be applied to real data. The class is divided into lectures that explain different methods and laboratory classes where students analyze real data. Examples focus on neural spiking activity but we also discuss other types of signals including MEG signals and local field potentials.

Resource(s):

Meyers, E. The Neural Decoding Toolbox. Frontiers in Neuroinformatics, 7:8, 2013, http://www.readout.info/