How does the brain compute with probabilities?

TL;DR

This paper reviews how neural activity might represent probability distributions, comparing three main hypotheses and discussing empirical evidence and future research directions.

Contribution

It provides a unified framework for understanding probabilistic neural coding proposals and critically evaluates existing empirical evidence.

Findings

Identifies similarities and differences among PPCs, DDCs, and NSCs.

Analyzes empirical data in the context of competing hypotheses.

Proposes future research directions combining theory and experiments.

Abstract

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.