The ability to record activities from hundreds of neurons simultaneously in the brain has placed an increasing demand for developing appropriate statistical techniques to analyze such data. Recently, deep generative models have been proposed to fit neural population responses. While these methods are flexible and expressive, the downside is that they can be difficult to interpret and identify. To address this problem, we propose a method that integrates key ingredients from latent models and traditional neural encoding models. Our method, pi-VAE, is inspired by recent progress on identifiable variational auto-encoder, which we adapt to make appropriate for neuroscience applications. Specifically, we propose to construct latent variable models of neural activity while simultaneously modeling the relation between the latent and task variables (non-neural variables, e.g.

Additional Feedback: On Reproducibility: - I think the basic methodology could be replicated, but it would have been nice to include code as a supplementary material. I hope the authors can assure me that the code will be documented and made available upon publication. On The Motor Cortex Dataset: - The VAE and pi-VAE seem to perform similarity in panel i and panel m - The better performance of pi-VAE in panel h vs l is likely due to the input variable "u" which forces different latent represenations (update: after writing this, I noticed that this is indeed the case based on supplementary figure S1; though pi-VAE is still slightly better). This is fine, but perhaps makes the result unsurprising -- wouldn't other supervised methods (e.g. On The Hippocampal Dataset: - For fig 4B, I think that linear discriminant analysis (LDA) would be sufficient to get you separation between the two running directions --- i.e. this would recover "latent 1".

The ability to record activities from hundreds of neurons simultaneously in the brain has placed an increasing demand for developing appropriate statistical techniques to analyze such data. Recently, deep generative models have been proposed to fit neural population responses. While these methods are flexible and expressive, the downside is that they can be difficult to interpret and identify. To address this problem, we propose a method that integrates key ingredients from latent models and traditional neural encoding models. Our method, pi-VAE, is inspired by recent progress on identifiable variational auto-encoder, which we adapt to make appropriate for neuroscience applications.