Before revision: Here I introduce an extension to demixed principal component analysis (dPCA), a  linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components .
After revision: Many studies of neural activity in behaving animals aim to discover interpretable low-dimensional structure in large-scale neural population recordings. One approach to this problem is demixed principal component analysis (dPCA), a linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components .