The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
What Does the Retina Know about Natural Scenes?
Abstract
By examining the experimental data on the statistical properties of natural scenes together with (retinal) contrast sensitivity data, we arrive at a first principle, theoretical hypothesis for the purpose of retinal processing and its relationship to an animal's environment. We argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex, as suggested by Barlow. The extent of this whitening of the input is limited, however, by the need to suppress input noise. Our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance: they predict an approximate Weber law at low spatial frequencies and a De Vries-Rose law at high frequencies. Assuming that the dominant source of noise is quantum, we generate a family of contrast sensitivity curves as a function of mean luminance. This family is compared to psychophysical data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Predictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042 · cited 1×
“Such a limitation may be a physical one in the retina such as at the bipolar cell input sYnapses or it may be in the ganglion cell output cable, the optic nerve (see also Srinivisan et al. 1982).”
Background
Cited by
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
Could information theory provide an ecological theory of sensory processing?
Atick, J. J. (2011) · Network: Computation in Neural Systems · doi:10.3109/0954898x.2011.638888 · Cited by 679
Self-citationVisual features of intermediate complexity and their use in classification
Ullman, S., Vidal-Naquet, M., & Sali, E. (2002) · Nature Neuroscience · doi:10.1038/nn870 · Cited by 600
Dynamic predictive coding by the retina
Hosoya, T., Baccus, S. A., & Meister, M. (2005) · Nature · doi:10.1038/nature03689 · Cited by 468
Ecological statistics of Gestalt laws for the perceptual organization of contours
Elder, J. H., & Goldberg, R. M. (2002) · Journal of Vision · doi:10.1167/2.4.5 · Cited by 333
Predictive information in a sensory population
Palmer, S. E., Marre, O., Berry, M. J., & Bialek, W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506855112 · Cited by 257
Computational and dynamic models in neuroimaging
Friston, K. J., & Dolan, R. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.068 · Cited by 131
Bayesian natural selection and the evolution of perceptual systems
Geisler, W. S., & Diehl, R. L. (2002) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2001.1055 · Cited by 101
Coding of the contrasts in natural images by populations of neurons in primary visual cortex (V1)
Clatworthy, P. L., Chirimuuta, M., Lauritzen, J. S., & Tolhurst, D. J. (2003) · Vision Research · doi:10.1016/s0042-6989(03)00277-3 · Cited by 54
A Neural Representation of Naturalistic Motion-Guided Behavior in the Zebrafish Brain
Yildizoglu, T., Riegler, C., Fitzgerald, J. E., & Portugues, R. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.043 · Cited by 34
From water to land: Evolution of photoreceptor circuits for vision in air
Baden, T. (2024) · PLOS Biology · doi:10.1371/journal.pbio.3002422 · Cited by 21
Metadata
- Authors
- Atick, J. J., & Redlich, A. N.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1992-03
- Volume
- 4
- Issue
- 2
- Pages
- 196-210
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2042925217
- Cited by
- 705
- References
- 1
- FWCI
- 7.67
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
