Before revision:  However, kdPCA can still recover interpretable components from such data. Additionally, I apply kdPCA to a neural population previously analyzed by dPCA from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity. The components recovered by kdPCA achieve better generalization and demixing performance compared to dPCA by accounting for a nonlinear interaction between stimulus and decision in the neural activity. In conclusion, simple nonlinear interactions inhibit the ability of linear dimensionality reduction techniques to recover interpretable demixed components in neural data, but this problem can be tackled by nonlinear dimensionality reduction approaches like kdPCA .
After revision:  However, kdPCA can still recover interpretable components in these examples. Finally, I demonstrate kdPCA using two examples of neural populations recorded during perceptual decision-making tasks .