Reviews: Model-based targeted dimensionality reduction for neuronal population data

Supervised dimensionality reduction has become a topic of interest in the systems neuroscience community over the last few years. Here, the authors suggested a very sensible extension to demixed PCA and targeted dimensionality reduction (TDR), which are recently developed but well-known and impactful methods in the field. However, I am disappointed that it heavily relies on simulated data rather than real biological datasets for its results. In particular, all datasets examined by the demixed PCA paper (in eLife) are freely available, so I feel that at least one of those datasets should have been analyzed for the purpose of comparison. I am not convinced that the proposed model would produce qualitatively different results from those already published. That being said, I think the proposed modeling framework is more straightforward than demixed PCA and offers the possibility of interesting future extensions.