Toward a unified theory of efficient, predictive, and sparse coding.

ABSTRACT: A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, "efficient coding" posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We

SUBMITTER: Chalk M 

PROVIDER: S-EPMC5776796 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature