Demixed Principal Component Analysis

Demixed PCA (dPCA) is a linear dimensionality reduction technique that can help to automatically discover and highlight the essential features of complex population activities. The population activity is decomposed into a few components that capture most of the variance in the data and that highlight the dynamic tuning of the population to various task parameters, such as stimuli, decisions, rewards, etc.

D Kobak+, W Brendel+, C Constantinidis, CE Feierstein, A Kepecs, ZF Mainen, X-L Qi, R Romo, N Uchida, CK Machens
Demixed principal component analysis of neural population data
eLife 2016,
(arXiv link:

Python or MATLAB code for dPCA is available at