ArticleJournal of vision2019
The statistics of how natural images drive the responses of neurons.
Article in Journal of vision, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Bandwidth of gamma-distribution-shaped functions via Lambert W function.Statistics & probability letters · 2026Article
- How distinct sources of nuisance variability in natural images and scenes limit human stereopsis.PLoS computational biology · 2025Article
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Feature-specific divisive normalization improves natural image encoding for depth perception.bioRxiv : the preprint server for biology · 2024Article
- Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.Journal of vision · 2024Article
- Learning divisive normalization in primary visual cortex.PLoS computational biology · 2021Article
- Natural scene statistics predict how humans pool information across space in surface tilt estimation.PLoS computational biology · 2020Article
- Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
To model the responses of neurons in the early visual system, at least three basic components are required: a receptive field, a normalization term, and a specification of encoding noise. Here, we examine how the receptive field, the normalization factor, and the encoding noise affect the drive to model-neuron responses when stimulated with natural images. We show that when these components are modeled appropriately, the response drives elicited by natural stimuli are Gaussian-distributed and scale invariant, and very nearly maximize the sensitivity (d') for natural-image discrimination. We discuss the statistical models of natural stimuli that can account for these response statistics, and we show how some commonly used modeling practices may distort these results. Finally, we show that normalization can equalize important properties of neural response across different stimulus types. Specifically, narrowband (stimulus- and feature-specific) normalization causes model neurons to yield Gaussian response-drive statistics when stimulated with natural stimuli, 1/f noise stimuli, and white-noise stimuli. The current work makes recommendations for best practices and lays a foundation, grounded in the response statistics to natural stimuli, upon which to build principled models of more complex visual tasks.
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Registered trials
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