Variance predicts salience in central sensory processing

Ann M. Hermundstad, John J. Briguglio, Mary M. Conte, Jonathan D. Victor, Vijay Balasubramanian, Gašper Tkačik

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independentlymeasured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.

Original languageEnglish
Article numbere03722
Pages (from-to)1-28
Number of pages28
Issue numberNovember
StatePublished - 14 Nov 2014
Externally publishedYes


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