Computational Learning and Memory Group

The redemption of noise: inference with neural populations
Trends in Neurosciences (2018)
R Echeveste, and M Lengyel


This is an invited commentary on Ma et al., Nature Neuroscience (2006).

In 2006, Ma et al. 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.