PT  - JOURNAL ARTICLE
AU  - Richard D. Lange
AU  - Sabyasachi Shivkumar
AU  - Ankani Chattoraj
AU  - Ralf M. Haefner
TI  - Bayesian Encoding and Decoding as Distinct Perspectives on Neural Coding
AID  - 10.1101/2020.10.14.339770
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.14.339770
4099  - http://biorxiv.org/content/early/2020/10/15/2020.10.14.339770.short
4100  - http://biorxiv.org/content/early/2020/10/15/2020.10.14.339770.full
AB  - 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 StatementThe authors have declared no competing interest.