Before revision:  Additionally, I compare kdPCA and dPCA to a neural population  from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity  .
After revision:  Additionally, I compare kdPCA and dPCA to a neural population 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 .