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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Relations between the statistics of natural images and the response properties of cortical cells
Field, D. J. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.002379 · Cited by 3,241
(1961) · Information and Control · doi:10.1016/s0019-9958(61)80064-8 · Cited by 2,904
DISCHARGE PATTERNS AND FUNCTIONAL ORGANIZATION OF MAMMALIAN RETINA
Kuffler, S. W. (1953) · Journal of Neurophysiology · doi:10.1152/jn.1953.16.1.37 · Cited by 2,609
Blakemore, C. B., & Campbell, F. W. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008862 · Cited by 2,032
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
Barlow, H. B. (1953) · The Journal of Physiology · doi:10.1113/jphysiol.1953.sp004829 · Cited by 912
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
Chichilnisky, E. J. (2001) · Network Computation in Neural Systems · doi:10.1080/net.12.2.199.213 · Cited by 788
Cited by
Sorted by citations
Items 301–400 of 466
Sparse Spike Coding : applications of Neuroscience to the processing of natural imagesPreprint
Perrinet, L. (2008) · Cited by 7
Encoding surprise by retinal ganglion cells
Despotović, D., Joffrois, C., Marre, O., & Chalk, M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011965 · Cited by 6
Spatial and temporal correlations of spike trains in frog retinal ganglion cells
Liu, W., Jing, W., Li, H., Gong, H.-Q., & Liang, P.-J. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0277-9 · Cited by 6
Using unsupervised capsule neural network reveal spatial representations in the human brain
Wang, G., Jiang, N., Liu, T., Wang, L., Suo, D., Chen, D., Funahashi, S., & Yan, T. (2024) · Human Brain Mapping · doi:10.1002/hbm.26573 · Cited by 5
Learning probability distributions of sensory inputs with Monte Carlo predictive coding
Oliviers, G., Bogacz, R., & Meulemans, A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012532 · Cited by 10
Randomly oriented edge arrangements dominate naturalistic arrangements in binocular rivalry
Hunt, J. J., Mattingley, J. B., & Goodhill, G. J. (2012) · Vision Research · doi:10.1016/j.visres.2012.05.007 · Cited by 6
Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition
Kim, J. K., & Fiorillo, C. D. (2017) · Nature Communications · doi:10.1038/ncomms14566 · Cited by 6
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations
Sledge, I. J., & Príncipe, J. C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3115698 · Cited by 6
Psychiatric and Neurological Disorders
Catrambone, V., & Valenza, G. (2021) · Functional Brain-Heart Interplay · doi:10.1007/978-3-030-79934-2_5 · Cited by 4
Evolving a dynamic predictive coding mechanism for novelty detection
Haggett, S. J., Chu, D., & Marshall, I. W. (2007) · Knowledge-Based Systems · doi:10.1016/j.knosys.2007.11.007 · Cited by 6
Ionic nanoarchitectonics for electronic information devices
Terabe, K., Tsuchiya, T., Tsuruoka, T., Tanaka, H., Valov, I., Gimzewski, J. K., & Hasegawa, T. (2025) · Solid State Ionics · doi:10.1016/j.ssi.2025.116995 · Cited by 5
Towards human-leveled vision systems
Ding, J., & Huang, T. (2024) · Science China Technological Sciences · doi:10.1007/s11431-024-2762-5 · Cited by 5
Wispinski, N. J., Stone, S. A., Bertrand, J. K., Zuk, A. A. O., Lavoie, E. B., Gallivan, J. P., & Chapman, C. S. (2021) · Cortex · doi:10.1016/j.cortex.2021.02.010 · Cited by 5
Toward unsupervised outbreak detection through visual perception of new patterns
Lévy, P., & Valleron, A. (2009) · BMC Public Health · doi:10.1186/1471-2458-9-179 · Cited by 5
Modeling disinhibition within a layered structure of the LGMD neuron
Silva, A., & Santos, C. P. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6707010 · Cited by 5
Fractionally Predictive Spiking Neurons
Rombouts, J. O., & Bohté, S. M. (2010) · neural information processing systems · Cited by 5
Steinmetz, S., Layton, O. W., Powell, N. V., & Fajen, B. R. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.844289 · Cited by 5
Zhang, D., Wu, S., & Rasch, M. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118125 · Cited by 5
Times of Life and Timing in Developmental Psychology
Hood, K. E. (2006) · Culture & Psychology · doi:10.1177/1354067x06064593 · Cited by 5
A Predictive Coding Framework for Learning to Predict Changes in Streaming Data
Banerjee, B., & Dutta, J. K. (2013) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2013.134 · Cited by 5
Visual Processing in the Primate Brain
Baker, C. I. (2012) · Wiley eBooks · doi:10.1002/9781118133880.hop203004 · Cited by 6
Fractionally Predictive Spiking NeuronsPreprint
Bohté, S. M., & Rombouts, J. O. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.6178 · Cited by 5
Dynamical neural networks: Modeling low-level vision at short latencies
Perrinet, L. (2007) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2007-00061-7 · Cited by 6
Neurons on a Chip ? Toward High Throughput Network and Pharmacology Investigations
Nagarah, J. M. (2011) · Frontiers in Pharmacology · doi:10.3389/fphar.2011.00074 · Cited by 4
Analytical Results for the Error in Filtering of Gaussian Processes
Susemihl, A., Meir, R., & Opper, M. (2011) · Cited by 4
Rapid adaptation of primate LGN neurons to drifting grating stimulation
Daumail, L., Carlson, B. M., Mitchell, B., Cox, M. A., Westerberg, J. A., Johnson, C., Martin, P. R., Tong, F., Maier, A., & Dougherty, K. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00058.2022 · Cited by 3
Rapid calibration to dynamic temporal contexts
Rhodes, D., Bridgewater, T., Ayache, J., & Riemer, M. (2023) · Quarterly Journal of Experimental Psychology · doi:10.1177/17470218231219507 · Cited by 5
Expectations and visual aftereffects
Pinchuk-Yacobi, N., Dekel, R., & Sagi, D. (2016) · Journal of Vision · doi:10.1167/16.15.19 · Cited by 4
Circuits and Properties of Signal Transmission in the Retina
Witkovsky, P., Stell, W. K., & Ishida, A. T. (2006) · Journal of Neurophysiology · doi:10.1152/classicessays.00040.2006 · Cited by 4
Sensory Systems: Fine-Tuning the Visual Scene
Masland, R. H. (2005) · Current Biology · doi:10.1016/j.cub.2005.09.023 · Cited by 4
Evolving novelty detectors for specific applications
Haggett, S. J., & Chu, D. (2009) · Neurocomputing · doi:10.1016/j.neucom.2008.12.009 · Cited by 4
Illusory Bands in Orientation and Spatial Frequency: A Cortical Analog to Mach Bands
Essock, E. A., Hansen, B. C., & Haun, A. (2007) · Perception · doi:10.1068/p5695 · Cited by 4
Simulation of Visual Attention Using Hierarchical Spiking Neural Networks
Wu, Q., McGinnity, T. M., Maguire, L., Cai, R., & Chen, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-24553-4_5 · Cited by 4
Adaptation in the Visual System: Networked Fatigue or Suppressed Prediction Error Signalling?Preprint
Feuerriegel, D. (2023) · doi:10.31234/osf.io/73hb4 · Cited by 4
Rethinking Predictive Processing
Furutachi, S., & Hofer, S. B. (2026) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102124-031410 · Cited by 3
Evidence and implications of abnormal predictive coding in dementia
Kocagoncu, E., Klimovich-Gray, A., Hughes, L. E., & Rowe, J. B. (2021) · Brain · doi:10.1093/brain/awab254 · Cited by 3
“There is direct evidence for predictive coding of vision (Hosoya et al., 2005; Hohwy et al., 2008), rhythmic perception (Vuust et al.”
BackgroundMaravall, M., & de Hoz, L. (2025) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2025.103139 · Cited by 3
Edge Detection Using Integrate and Fire Neuron
İncetaş, M. O., & Arslan, R. U. (2019) · Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.19113/sdufenbed.570597 · Cited by 3
Framing Effects in Object Perception
Ivy, S., & Mroczko-Wąsowicz, A. (2025) · Review of Philosophy and Psychology · doi:10.1007/s13164-024-00763-8 · Cited by 4
A Dynamic Model for Early Vision Processing
Ma, L., Zheng, N., & Yuan, Z. (2008) · International Conference on Computer and Electrical Engineering · doi:10.1109/iccee.2008.187 · Cited by 3
Adaptive conversion of real-valued input into spike trains
Hadjiivanov, A. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727314 · Cited by 3
Fridriksson, J., Rorden, C., Newman‐Norlund, R., Froeliger, B., & Thrasher, J. F. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00319 · Cited by 3
Matsumoto, A., & Tachibana, M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00979 · Cited by 3
Functional-pathway-dominant contrast adaptation and sensitization in mouse retinal ganglion cells
Dai, M., & Liang, P.-J. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09636-z · Cited by 3
Dynamics of Nonlinear Feedback Control
Snippe, H. P., & van Hateren, J. H. (2007) · Neural Computation · doi:10.1162/neco.2007.19.5.1179 · Cited by 3
Planet Braitenberg: Experiments in virtual psychology
Smart, P. R. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.06.001 · Cited by 3
Luminance Sensitivity and Contrast Detection
Kaplan, E. (2008) · Elsevier eBooks · doi:10.1016/b978-012370880-9.00294-2 · Cited by 3
Human Face Recognition and Image Statistics using Matlab
Keil, M. S. (2011) · Wiley eBooks · doi:10.1002/9783527635245.ch36 · Cited by 3
Sufficient encoding of dynamical systems
Creutzig, F. (2008) · edoc Publication server (Humboldt University of Berlin) · doi:10.18452/15817 · Cited by 3
From Neuronal Models to Neuronal Dynamics and Image Processing
Keil, M. S. (2015) · Wiley eBooks · doi:10.1002/9783527680863.ch10 · Cited by 3
Introduction to the Special Issue: Situated Social Cognition
Jonas, K. J., & Cesario, J. (2013) · Social Cognition · doi:10.1521/soco.2013.31.2.119 · Cited by 3
Modeling non-standard retinal in/out function using computer vision variational methods
Teftef, E., Escobar, M.-J., Astudillo, A., Carvajal, C., Cessac, B., Palacios, A. G., Viéville, T., & Alexandre, F. (2013) · HAL · Cited by 4
Linear response for spiking neuronal networks with unbounded memoryPreprint
Cessac, B., Ampuero, I., & Cofré, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.05344 · Cited by 3
ODE-Inspired Analysis for the Biological Version of Oja's Rule in Solving Streaming PCAPreprint
Chou, C.-N., & Wang, M. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02363 · Cited by 3
Brochard, J., & Jean Daunizeau, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.08.287714 · Cited by 3
Computational Models of Neural Retina
Dokos, S., & Guo, T. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_652 · Cited by 3
Structure and function in the predictive brain
Facchin, M., & Viola, M. (2025) · Biology & Philosophy · doi:10.1007/s10539-025-10000-w · Cited by 3
Contributions of Bipolar Cells to Ganglion Cell Receptive Fields
Freed, M. A. (2008) · Elsevier eBooks · doi:10.1016/b978-012370880-9.00271-1 · Cited by 2
After-image formation by adaptation to dynamic color gradients
Alzeer, M. S., Houwers, K., van der Smagt, M. J., der Stigchel, S. V., & Naber, M. (2022) · Attention Perception & Psychophysics · doi:10.3758/s13414-022-02570-8 · Cited by 2
Görsel Yanılsamalar Bağlamında Görsel Algının Esasları
Ayhan, İ., & Ünal, G. (2020) · Nesne Psikoloji Dergisi · doi:10.7816/nesne-08-18-11 · Cited by 2
Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control
Keil, M. S., & Vitria, J. M. (2006) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2007/51684 · Cited by 2
A Novel Dual-Probe Adaptive Model for Image Change Detection
Ma, L., Zheng, N., Yuan, Z., & Zhang, X. (2010) · IEEE Signal Processing Letters · doi:10.1109/lsp.2010.2063023 · Cited by 2
Synchronized Firings in Retinal Ganglion Cells in Response to Natural Stimulation
Zhang, Y., Xiao, L., Liu, W., Gong, H.-Q., & Liang, P.-J. (2011) · Chinese Physics Letters · doi:10.1088/0256-307x/28/2/028701 · Cited by 2
Defining statistical perceptions with an empirical Bayesian approach
Tajima, S. (2013) · Physical Review E · doi:10.1103/physreve.87.042707 · Cited by 2
Methods for Building Network Models of Neural Circuits
DePasquale, B. (2016) · doi:10.7916/d8w09600 · Cited by 2
Weissglass, D. E. (2019) · Philosophical Studies · doi:10.1007/s11098-019-01305-0 · Cited by 2
Adaptation of the inferior temporal neurons and efficient visual processing
Yamane, Y. (2024) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2024.1398874 · Cited by 2
Effects of surround suppression on response adaptation of V1 neurons to visual stimuli.
Li, P., Jin, C.-H., Jiang, S., Li, M., Wang, Z.-L., Zhu, H., Chen, C., & Hua, T. (2014) · PubMed Central · doi:10.13918/j.issn.2095-8137.2014.5.411 · Cited by 2
Emergent Robotic Personality Traits via Agent-Based Simulation of Abstract Social Environments
Bennett, C. C. (2021) · Information · doi:10.3390/info12030103 · Cited by 2
Dynamic Adaptation in Fly Motion Vision
Flanagin, V. L. (2006) · Elektronische Hochschulschriften der LMU München (Ludwig-Maximilians-Universität München) · doi:10.5282/edoc.5980 · Cited by 2
Long-range sensitization in the vertebrate retina and human perceptionPreprint
Naecker, B. N., & Baccus, S. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/492033 · Cited by 2
Self-citationPredictive coding underlies adaptation in the subcortical sensory pathwayPreprint
Tabas, A., Mihai, P. G., Kiebel, S. J., Trampel, R., & von Kriegstein, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11328 · Cited by 2
Intuition, expertise and judgement in the capture and assessment of photographic images
Ryan, R. J. (2019) · doi:10.46289/ssfa2043 · Cited by 2
Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapsesPreprint
Ebert, S., Buffet, T., Sermet, B. S., Marre, O., & Cessac, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.12.523643 · Cited by 2
Towards a multipurpose neural network approach to novelty detection
Haggett, S. J. (2021) · Kent Academic Repository (University of Kent) · doi:10.22024/unikent/01.02.86386 · Cited by 2
Behavioral alignment as an organizing principle in sensory codingPreprint
Huang, S., Portugues, R., & Fitzgerald, J. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703828 · Cited by 2
Experimental Demonstration of Image Edge Detection Using a Photonic Spiking DFB-SA Neuron
Yi, S., Yang, Y., Huang, Y., Zhou, P., Lau, K. Y., & Li, N. (2025) · IEEE Photonics Technology Letters · doi:10.1109/lpt.2025.3642138 · Cited by 1
A bioinspired photoelectrochemical synapse for neurotransmitter-mediated in-sensor computing
Kou, B., Cheng, Y., Han, Z. D., Xiao, A., Huang, Y., Zhao, W., & Xu, J. (2026) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2026.118951 · Cited by 1
A SURVEY OF BIOLOGICALLY INSPIRED IMAGE PROCESSING FOR OBJECTS RECOGNITION
Shi, W., Li, Z., Shi, X., & Zhong, Z. (2009) · International Journal of Image and Graphics · doi:10.1142/s021946780900354x · Cited by 1
Permanent Magnet Synchronous Motor Control Based on Retina Neural Network
Yang, J., Wu, Y., Yu, Y., & Zhao, W. (2010) · International Conference on E-Product E-Service and E-Entertainment · doi:10.1109/iceee.2010.5661508 · Cited by 1
A lattice filter model of the visual pathway
Gregor, K., & Chklovskii, D. B. (2012) · Cited by 1
Neuronal Risk Processing in Human and Monkey Prefrontal Cortex
Schultz, W. (2017) · Springer eBooks · doi:10.1007/978-4-431-56508-6_6 · Cited by 2
Çarıkçı, İ. H. (2016) · Ayrıntı Dergisi · Cited by 1
A Dynamic Region Generation Algorithm for Image Segmentation Based on Spiking Neural Network
Zuo, L., Ma, L., Xiao, Y., Zhang, M., & Qu, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70090-8_83 · Cited by 1
Playing in Streams of Knowledge: Multimodality in Perception and Action
Christensen, J. (2018) · Theory and history in the human and social sciences · doi:10.1007/978-3-319-92339-0_10 · Cited by 1
Johnson, P. A., Davies, S., & Hogendoorn, H. (2020) · Journal of Vision · doi:10.1167/jov.20.13.8 · Cited by 1
Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism?
Füllsack, M. (2013) · Constructivist Foundations · Cited by 1
Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli
Vystrčilová, M., Sridhar, S., Burg, M. F., Khani, M. H., Karamanlis, D., Schreyer, H. M., Ramakrishna, V., Krüppel, S., Zapp, S. J., Mietsch, M., et al. (2026) · eNeuro · doi:10.1523/eneuro.0060-26.2026 · Cited by 1
The Retina as a Dynamical System
Cessac, B. (2021) · World scientific series on nonlinear science, series B · doi:10.1142/9789811221903_0014 · Cited by 1
Information synergy in the anticipatory dynamics of a retina
Lin, Q.-R., Chou, P.-Y., & Chan, C. K. (2021) · Physical review. E · doi:10.1103/physreve.104.034420 · Cited by 1
Phase Transitions in When Feedback is Useful
Boominathan, L., & Pitkow, X. (2022) · neural information processing systems · doi:10.52202/068431-0788 · Cited by 1
Conceptual Change in Visual Neuroscience: The Receptive Field Concept
Venturelli, A. N. (2021) · International Studies in the Philosophy of Science · doi:10.1080/02698595.2021.2002675 · Cited by 1
Luminance Sensitivity and Contrast Detection
Kaplan, E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-805408-6.00294-3 · Cited by 1
Inferring Neuronal Network Connectivity using Time-constrained EpisodesPreprint
Patnaik, D., SASTRY, P. S., & Unnikrishnan, K. P. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0709.0218 · Cited by 1
Attention and awareness : visual psychophysics and aversive conditioning in humans
Tsuchiya, N. (2006) · doi:10.7907/jvp9-ta28 · Cited by 1
A qualitative model of the rod photoreceptor in natural contextPreprint
Dubuc, T. T., & Roesch, E. B. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/050823 · Cited by 1
The neural basis of temporal prediction and the role of dopamine
Fiorillo, C. D. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199563456.003.0020 · Cited by 1
Prediction in aging language processing
Cheimariou, S., McGregor, K., McMurray, B., Horne, A. O. V., Gordon, J. K., & Duff, M. C. (2016) · doi:10.17077/etd.9mkjnsvh · Cited by 1
Multisensory expectations shape olfactory input to the brainPreprint
Czarnecki, L. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.7282/t37p91v6 · Cited by 1
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
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
