ArticleNature communications2021
Neuronal variability reflects probabilistic inference tuned to natural image statistics.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Combining sampling and attractor dynamics in spiking models of head direction systems.PLoS computational biology · 2026Article
- Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex.bioRxiv : the preprint server for biology · 2026Article
- The time-intensity uncertainty principle in vision.Attention, perception & psychophysics · 2026Article
- Spike count analysis for multiplexing inference (SCAMPI).Journal of computational neuroscience · 2026Article
- Size tuning of neural response variability in laminar circuits of macaque primary visual cortex.eLife · 2026Article
- Increased Perceptual Reliability Reduces Membrane Potential Variability in Cortical Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- Multiple mechanisms of response suppression to self-induced sensation during pursuit eye movements.Royal Society open science · 2025Article
- Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Relating natural image statistics to patterns of response covariability in macaque primary visual cortex.Nature communications · 2025Article
- Sensory population activity reveals downstream confidence computations in the primate visual system.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Disentangling signal and noise in neural responses through generative modeling.PLoS computational biology · 2025Article
- A unified framework to model synaptic dynamics during the sleep-wake cycle.PLoS biology · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- Unstable foveation's impact on reading, object tracking, and its implications for diagnosing and intervening in reading difficulties.Scientific reports · 2025Article
- Deciphering neuronal variability across states reveals dynamic sensory encoding.Nature communications · 2025Article
- Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans.Nature communications · 2025Article
- Spike reliability is cell type specific and shapes excitation and inhibition in the cortex.Scientific reports · 2025Article
- Disentangling signal and noise in neural responses through generative modeling.bioRxiv : the preprint server for biology · 2024Article
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5 authors.
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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.
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