Divisive normalization is an efficient code for multivariate Pareto-distributed environments.

ABSTRACT: Divisive normalization is a canonical computation in the brain, observed across neural systems, that is often considered to be an implementation of the efficient coding principle. We provide a theoretical result that makes the conditions under which divisive normalization is an efficient code analytically precise: We show that, in a low-noise regime, encoding an n-dimensional stimulus via divisive normalization is efficient if and only if its prevalence in the environment is described by a multivariate Pareto distribution. We generalize this multivariate analog of histogram equalization to allow for arbitrary metabolic costs of the representation, and show how different assumptions on costs are associated with different shapes of the distributions that divisive normalization efficie

SUBMITTER: Bucher SF 

PROVIDER: S-EPMC9546555 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature