This paper looks really interesting to me, although after quickly reading the introduction it's evident that I'm going to have to invest quite a bit of time and

This paper looks really interesting to me, although after quickly reading the introduction it's evident that I'm going to have to invest quite a bit of time and effort on the paper to grasp its key ideas. I come from more an encoding-decoding, deep/machine-learning background, as opposed to a probabilistic graphical modeling or PGM background, and my knowledge of neuroscience is minimal. To date, my experience with "deep PGM models" (for lack of a better term) is limited to some tinkering with (a) variational autoencoders using ELBO maximization as the training objective, and to a much lesser extent (b) "bi-directional" GANs using a Jensen-Shannon divergence between two joint distributions as the training loss. Has anyone here with a similar background to mine had a chance to read this paper? Any thoughts?