Neuronal variability reflects probabilistic inference tuned to natural image statistics

Dylan Festa, Amir Aschner, Aida Davila, Adam Kohn, Ruben Coen-Cagli

Research output: Contribution to journalArticlepeer-review

Abstract

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses—their variability—can be explained by a probabilistic representation tuned to naturalistic inputs.

Original languageEnglish (US)
Article number3635
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 2021

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Fingerprint

Dive into the research topics of 'Neuronal variability reflects probabilistic inference tuned to natural image statistics'. Together they form a unique fingerprint.

Cite this