How does the brain compute with probabilities?
This work addresses a foundational question in neuroscience about probabilistic computations in the brain, but it is incremental as it synthesizes and clarifies existing theories rather than introducing new methods.
The paper tackles the problem of how neural activity represents probability distributions by addressing obstacles such as unifying language for hypotheses, explaining three prominent proposals, and reviewing empirical data to clarify debates.
This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations -- Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) -- and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and experiments.