Relating Divisive Normalization to Neuronal Response Variability

Ruben Coen-Cagli, Selina S. Solomon

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Cortical responses to repeated presentations of a sensory stimulus are variable. This variability is sensitive to several stimulus dimensions, suggesting that it may carry useful information beyond the average firing rate. Many experimental manipulations that affect response variability are also known to engage divisive normalization, a widespread operation that describes neuronal activity as the ratio of a numerator (representing the excitatory stimulus drive) and denominator (the normalization signal). Although it has been suggested that normalization affects response variability, we lack a quantitative framework to determine the relation between the two. Here we extend the standard normalization model, by treating the numerator and the normalization signal as variable quantities. The resulting model predicts a general stabilizing effect of normalization on neuronal responses, and allows us to infer the single-trial normalization strength, a quantity that cannot be measured directly. We test the model on neuronal responses to stimuli of varying contrast, recorded in primary visual cortex of male macaques. We find that neurons that are more strongly normalized fire more reliably, and response variability and pairwise noise correlations are reduced during trials in which normalization is inferred to be strong. Our results thus suggest a novel functional role for normalization, namely, modulating response variability. Our framework could enable a direct quantification of the impact of single-trial normalization strength on the accuracy of perceptual judgments, and can be readily applied to other sensory and nonsensory factors.

Original languageEnglish (US)
Pages (from-to)7344-7356
Number of pages13
JournalJournal of Neuroscience
Volume39
Issue number37
DOIs
StatePublished - 2019

Keywords

  • Divisive normalization
  • Modeling
  • Neuronal variability
  • Single-trial inference
  • Visual cortex

ASJC Scopus subject areas

  • Neuroscience(all)

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