Variance predicts salience in central sensory processing
Ann M Hermundstad,
John J Briguglio,
Mary M Conte,
Jonathan D Victor,
Vijay Balasubramanian,
Gašper Tkačik
Affiliations
Ann M Hermundstad
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States; Laboratoire de Physique Théorique, École Normale Supérieure, Paris, France
John J Briguglio
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States
Mary M Conte
Brain and Mind Research Institute, Weill Cornell Medical College, New York, United States
Jonathan D Victor
Brain and Mind Research Institute, Weill Cornell Medical College, New York, United States
Vijay Balasubramanian
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States; Laboratoire de Physique Théorique, École Normale Supérieure, Paris, France; Initiative for the Theoretical Sciences, City University of New York Graduate Center, New York, United States
Gašper Tkačik
Institute of Science and Technology Austria, Klosterneuburg, Austria
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 independently-measured 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.
