Variance predicts salience in central sensory processing.
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.
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States.
Ann M
AM
Hermundstad
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States.
John J
JJ
Briguglio
Brain and Mind Research Institute, Weill Cornell Medical College, New York, United States.
Mary M
MM
Conte
Brain and Mind Research Institute, Weill Cornell Medical College, New York, United States.
Jonathan D
JD
Victor
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States.
Vijay
V
Balasubramanian
Institute of Science and Technology Austria, Klosterneuburg, Austria.
Gašper
G
Tkačik
D006801:Humans; D011984:Sensory Receptor Cells / Q000502:physiology*; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology; D014796:Visual Perception
human; natural scene statistics; neural coding; neuroscience; normative theories; visual cortex