The Redemption of Noise: Inference with Neural Populations

In 2006, Ma et al. (Nat. Neurosci. 1006;9:1432–1438) presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them