Bayesian Encoding and Decoding as Distinct Perspectives on Neural Coding

The Bayesian Brain hypothesis, according to which the brain implements statistical algorithms, is one of the leading theoretical frameworks in neuroscience. There are two distinct underlying philosophies: one in which the brain recovers structures that exist in the world from sensory neural activity (decoding), and another in which it represents latent quantities in an internal model (encoding). We argue that an implicit disagreement on this point underlies much of the vigorous debate surrounding the neural implementation of statistical algorithms, in particular the difference between sampling-based and parametric distributional codes. To demonstrate the complementary nature of the two approaches, we have shown mathematically that encoding by sampling can be equivalently interpreted as decoding task variables in a manner consistent with linear probabilistic population codes (PPCs), a popular decoding approach. Ongoing research on the nature of Bayesian inference in the brain will benefit from making their philosophical stance explicit in order to avoid misunderstandings and false dichotomies.