ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2019
Relating Divisive Normalization to Neuronal Response Variability.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Size tuning of neural response variability in laminar circuits of macaque primary visual cortex.eLife · 2026Article
- Neuronal heterogeneity of normalization strength in a circuit model.Science advances · 2026Article
- Relating natural image statistics to patterns of response covariability in macaque primary visual cortex.Nature communications · 2025Article
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- Early versus late noise differentially enhances or degrades context-dependent choice.Nature communications · 2025Article
- High neural noise in autism: A hypothesis currently at the nexus of explanatory power.Heliyon · 2024Review
- Adult neurogenesis improves spatial information encoding in the mouse hippocampus.Nature communications · 2024Article
- Signal switching may enhance processing power of the brain.Trends in cognitive sciences · 2024Review
- How does V1 population activity inform perceptual certainty?Journal of vision · 2024Article
- Response sub-additivity and variability quenching in visual cortex.Nature reviews. Neuroscience · 2024Review
- A dynamic attractor network model of memory formation, reinforcement and forgetting.PLoS computational biology · 2023Article
- Modeling the diverse effects of divisive normalization on noise correlations.PLoS computational biology · 2023Article
- Unsupervised approach to decomposing neural tuning variability.Nature communications · 2023Article
- Article
- Divisive normalization is an efficient code for multivariate Pareto-distributed environments.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Divisive normalization unifies disparate response signatures throughout the human visual hierarchy.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Neuronal variability reflects probabilistic inference tuned to natural image statistics.Nature communications · 2021Article
- Article
- Explaining Orientation Adaptation in V1 by Updating the State of a Spatial Model.Frontiers in computational neuroscience · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
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.
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