  Our ability to generalize beyond training data to novel, out-of-distribution,
image degradations is a hallmark of primate vision. The predictive brain,
exemplified by predictive coding networks (PCNs), has become a prominent
neuroscience theory of neural computation. Motivated by the recent successes of
variational autoencoders (VAEs) in machine learning, we rigorously derive a
correspondence between PCNs and VAEs. This motivates us to consider iterative
extensions of VAEs (iVAEs) as plausible variational extensions of the PCNs. We
further demonstrate that iVAEs generalize to distributional shifts
significantly better than both PCNs and VAEs. In addition, we propose a novel
measure of recognizability for individual samples which can be tested against
human psychophysical data. Overall, we hope this work will spur interest in
iVAEs as a promising new direction for modeling in neuroscience.
