The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Dynamic predictive coding by the retina
Abstract
Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.
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In the citing paper
“In this way, ' Ganglion cells signal not the raw visual image but the departures from the predictable structure, under the assumption of spatial and temporal uniformity ' (Hosoya et al. 2005, p. 71).”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Hosoya et al. 2005
In the citing paper
“Putting salamanders and rabbits into varying environments, and recording from their retinal ganglion cells, Hosoya et al. con fi rmed their hypothesis: Within a space of several seconds, about 50% of the ganglion cells altered their behaviors to keep step with the changing image statistics of the varying environments.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Hosoya et al. 2005
In the citing paper
“What this means, in each case, is that neural circuits predict, on the basis of local image characteristics, the likely image characteristics of nearby spots in space and time (basically, assuming that nearby spots will display similar image intensities) and subtract this predicted value from the actual value.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Hosoya et al. 2005
In the citing paper
“The starting point of this account is the well-established sense in which retinal ganglion cells take part in some form of predictive coding, insofar as their receptive fi elds display center-surround spatial antagonism, as well as a kind of temporal antagonism.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Hosoya et al. 2005
Citations
Cites
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The fundamental plan of the retina
Masland, R. H. (2001) · Nature Neuroscience · doi:10.1038/nn0901-877 · Cited by 1,247
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×
“In addition, there are inhibitory connections—mediated by amacrine cells—that are plastic as determined by the above correlation-based rule (equations (3) and (4); see Methods).”
BackgroundGraham, N. (1989) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195051544.001.0001 · Cited by 953
Summation and inhibition in the frog's retina
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Efficiency and ambiguity in an adaptive neural code
Fairhall, A. L., Lewen, G. D., Bialek, W., & van Steveninck, R. R. D. R. (2001) · Nature · doi:10.1038/35090500 · Cited by 847
A simple white noise analysis of neuronal light responses
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Cited by
Sorted by citations
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
Weak pairwise correlations imply strongly correlated network states in a neural population
Schneidman, E., Berry, M. J., Segev, R., & Bialek, W. (2006) · Nature · doi:10.1038/nature04701 · Cited by 1,745
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649
Neuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126
The Neuronal Organization of the Retina
Masland, R. H. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.002 · Cited by 1,245
Metadata
- Authors
- Hosoya, T., Baccus, S. A., & Meister, M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Meister, Markus
- Venue
- Nature
- Published
- 2005-07
- Volume
- 436
- Issue
- 7047
- Pages
- 71-77
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2120238402
- Cited by
- 468
- References
- 40
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 18 · 2023: 15 · 2022: 21 · 2021: 37 · 2020: 26 · 2019: 18 · 2018: 16 · 2017: 26 · 2016: 31 · 2015: 22 · 2014: 15 · 2013: 33 · 2012: 22
- FWCI
- 13.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- predictive coding, retinal ganglion cells, adaptive coding, spatiotemporal receptive fields, visual statistics, synaptic plasticity, salamander retina, rabbit retina, feature suppression, novelty detection
- MeSH terms
- Adaptation, Physiological, Animals, Models, Neurological, Neuronal Plasticity, New Zealand, Photic Stimulation, Rabbits, Retina, Retinal Ganglion Cells, Synapses, Time Factors, Urodela, Caudata
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