Abstract
The Bayesian Brain hypothesis, according to which the brain implements statistically optimal algorithms, is one of the leading theoretical frameworks in neuroscience. There are two distinct underlying philosophies: one in which the brain recovers experimenter-defined structures 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 some of the 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. Awareness of these differences in perspective helps misunderstandings and false dichotomies, and future research will benefit from an explicit discussion of the relative advantages and disadvantages of either approach to constructing models.
Competing Interest Statement
The authors have declared no competing interest.
