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Natural Image Statistics and Neural Representation
Abstract
▪ Abstract It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954) , Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.
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Metadata
- Authors
- Simoncelli, E. P., & Olshausen, B. A.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of California, Davis (US)
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2001-03
- Volume
- 24
- Issue
- 1
- Pages
- 1193-1216
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2151035455
- Cited by
- 2,460
- References
- 87
- Citations by year
- 2026: 99 · 2025: 135 · 2024: 114 · 2023: 130 · 2022: 117 · 2021: 133 · 2020: 108 · 2019: 136 · 2018: 107 · 2017: 98 · 2016: 118 · 2015: 110 · 2014: 122 · 2013: 124 · 2012: 93
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r29mh057921-05)
- National Institute of Mental Health (R29-MH57921)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- natural image statistics, efficient coding hypothesis, coding efficiency, neural representations, sensory adaptation, information theory, population coding, visual neurons
- MeSH terms
- Animals, Brain Mapping, Environment, Humans, Image Processing, Computer-Assisted, Neurons, Pattern Recognition, Visual, Visual Cortex, Visual Perception
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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