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 101–200 of 466
Glutamate Spillover between Mammalian Cone Photoreceptors
Szmajda, B. A., & DeVries, S. H. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2105-11.2011 · Cited by 68
Nonassociative learning as gated neural integrator and differentiator in stimulus-response pathways
Poon, C., & Young, D. L. (2006) · Behavioral and Brain Functions · doi:10.1186/1744-9081-2-29 · Cited by 66
A Retinal Mechanism Inspired Color Constancy Model
Zhang, X.-S., Gao, S., Li, R.-X., Du, X., Li, C., & Li, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2516953 · Cited by 68
Perrett, D. I., Xiao, D., Barraclough, N. E., Keysers, C., & Oram, M. W. (2009) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470210902959279 · Cited by 67
A Systematic Literature Review of the Successors of “NeuroEvolution of Augmenting Topologies”
Papavasileiou, E., Cornelis, J. P., & Jansen, B. (2020) · Evolutionary Computation · doi:10.1162/evco_a_00282 · Cited by 68
Inter-mosaic coordination of retinal receptive fields
Roy, S., Jun, N. Y., Davis, E. L., Pearson, J., & Field, G. D. (2021) · Nature · doi:10.1038/s41586-021-03317-5 · Cited by 64
A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex
Meirhaeghe, N., Sohn, H., & Jazayeri, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.025 · Cited by 69
Keil, M. S. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000329 · Cited by 67
Hogendoorn, H., & Burkitt, A. N. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.063 · Cited by 62
Predictive Coding with Neural Transmission Delays: A Real-Time Temporal Alignment Hypothesis
Hogendoorn, H., & Burkitt, A. N. (2019) · eNeuro · doi:10.1523/eneuro.0412-18.2019 · Cited by 61
Visual adaptation and novelty responses in the superior colliculus: Adaptation in the SC
Boehnke, S. E., Berg, D. J., Marino, R. A., Baldi, P. F., Itti, L., & Munoz, D. P. (2011) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2011.07805.x · Cited by 60
Nayebi, A., Sagastuy-Breña, J., Bear, D. M., Kar, K., Kubilius, J., Ganguli, S., Sussillo, D., DiCarlo, J. J., & Yamins, D. L. K. (2022) · Neural Computation · doi:10.1162/neco_a_01506 · Cited by 64
Using Synchronization for Prediction of High-Dimensional Chaotic Dynamics
Cohen, A. B., Ravoori, B., Murphy, T. E., & Roy, R. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.154102 · Cited by 59
Pattern Adaptation and Normalization Reweighting
Westrick, Z. M., Heeger, D. J., & Landy, M. S. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1067-16.2016 · Cited by 54
Zhang, C., Xu, F., Zhao, X., Zhang, M., Han, W., Yu, H., Wang, S., Yang, Y., Tong, Y., Tang, Q., et al. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107001 · Cited by 57
Werner, A. (2014) · Vision Research · doi:10.1016/j.visres.2014.10.005 · Cited by 56
Bayesian Perception Is Ecological Perception
Orlandi, N. (2016) · Philosophical Topics · doi:10.5840/philtopics201644226 · Cited by 60
On the joys of perceiving: Affect as feedback for perceptual predictions
Четвериков, А. А., & Kristjansson, A. K. (2016) · Acta Psychologica · doi:10.1016/j.actpsy.2016.05.005 · Cited by 53
Novel stimuli evoke excess activity in the mouse primary visual cortex
Homann, J., Koay, S. A., Chen, K. S., Tank, D. W., & Berry, M. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2108882119 · Cited by 52
Predictive Coding and the Slowness Principle: An Information-Theoretic Approach
Creutzig, F., & Sprekeler, H. (2007) · Neural Computation · doi:10.1162/neco.2008.01-07-455 · Cited by 54
Edge Detection Based on Spiking Neural Network Model
Wu, Q., McGinnity, T. M., Maguire, L., Belatreche, A., & Glackin, B. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74205-0_4 · Cited by 54
Sharpee, T. O., Miller, K. D., & Stryker, M. P. (2008) · Journal of Neurophysiology · doi:10.1152/jn.01397.2007 · Cited by 54
Symbolic Models and Emergent Models: A Review
Weng, J. (2011) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2011.2159113 · Cited by 50
Towards a General Theory of Neural Computation Based on Prediction by Single Neurons
Fiorillo, C. D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003298 · Cited by 50
Signatures of criticality arise from random subsampling in simple population models
Nonnenmacher, M., Behrens, C., Berens, P., Bethge, M., & Macke, J. H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005718 · Cited by 44
Sawtell, N. B. (2016) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021115-105003 · Cited by 56
Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans
Solomon, S. S., Tang, H., Sussman, E. S., & Kohn, A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab014 · Cited by 54
Generative models for active vision
Parr, T., Sajid, N., Da Costa, L., Mirza, M. B., & Friston, K. J. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.651432 · Cited by 46
Liu, J. K., & Gollisch, T. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004425 · Cited by 49
An Algorithmic Approach to Natural Behavior
Hein, A. M., Altshuler, D. L., Cade, D. E., Liao, J. C., Martin, B. T., & Taylor, G. K. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.018 · Cited by 54
Does face image statistics predict a preferred spatial frequency for human face processing?
Keil, M. S. (2008) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2008.0486 · Cited by 55
Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex
Díaz-Quesada, M., & Maravall, M. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.4931-07.2008 · Cited by 48
Affect-biased attention and predictive processing
Ransom, M., Fazelpour, S., Markovic, J., Kryklywy, J. H., Thompson, E. T., & Todd, R. M. (2020) · Cognition · doi:10.1016/j.cognition.2020.104370 · Cited by 49
Effects of prior information on decoding degraded speech: An fMRI study
Clos, M., Langner, R., Meyer, M. E., Oechslin, M. S., Zilles, K., & Eickhoff, S. B. (2012) · Human Brain Mapping · doi:10.1002/hbm.22151 · Cited by 47
Perception and Reality: Why a Wholly Empirical Paradigm is Needed to Understand Vision
Purves, D., Morgenstern, Y., & Wojtach, W. T. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00156 · Cited by 49
Abstract rules drive adaptation in the subcortical sensory pathway
Tabas, A., Mihai, P. G., Kiebel, S. J., Trampel, R., & von Kriegstein, K. (2020) · eLife · doi:10.7554/elife.64501 · Cited by 48
Betka, S., Adler, D., Similowski, T., & Blanke, O. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2022.108329 · Cited by 52
A Synaptic Mechanism for Temporal Filtering of Visual Signals
Baden, T., Nikolaev, A., Esposti, F., Dreosti, E., Odermatt, B., & Lagnado, L. (2014) · PLoS Biology · doi:10.1371/journal.pbio.1001972 · Cited by 45
Optimal Prediction in the Retina and Natural Motion Statistics
Salisbury, J. M., & Palmer, S. E. (2016) · Journal of Statistical Physics · doi:10.1007/s10955-015-1439-y · Cited by 47
Saha, D., Sun, W., Li, C., Nizampatnam, S., Padovano, W. M., Chen, Z., Chen, A., Altan, E., Lo, R., Barbour, D. L., et al. (2017) · Nature Communications · doi:10.1038/ncomms15413 · Cited by 41
A visual attention model based on hierarchical spiking neural networks
Wu, Q., McGinnity, T. M., Maguire, L., Cai, R., & Chen, M. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.01.046 · Cited by 43
Nonlinear Dynamics Support a Linear Population Code in a Retinal Target-Tracking Circuit
Leonardo, A. M., & Meister, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2257-13.2013 · Cited by 43
Self-citationTransformation of Visual Signals by Inhibitory Interneurons in Retinal Circuits
Jadzinsky, P. D., & Baccus, S. A. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170315 · Cited by 41
Self-citationCone selectivity derived from the responses of the retinal cone mosaic to natural scenes
Wächtler, T., Doi, E., Lee, T.-W., & B, T. J. S. A. (2007) · Journal of Vision · doi:10.1167/7.8.6 · Cited by 43
Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits
Puccini, G. D., Sanchez-Vives, M. V., & Compte, A. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030082 · Cited by 46
Contrast adaptation induced by defocus – A possible error signal for emmetropization?
Ohlendorf, A., & Schaeffel, F. (2008) · Vision Research · doi:10.1016/j.visres.2008.10.016 · Cited by 42
Establishment of a Scaffold for Orientation Maps in Primary Visual Cortex of Higher Mammals
Grabska‐Barwińska, A., & von der Malsburg, C. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5514-06.2008 · Cited by 42
Modulation of Perception and Brain Activity by Predictable Trajectories of Facial Expressions
Nicholas Furl, A., van Rijsbergen, N. J., Kiebel, S. J., Friston, K. J., Treves, A., & Dolan, R. J. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp140 · Cited by 45
Local and Global Contrast Adaptation in Retinal Ganglion Cells
Garvert, M. M., & Gollisch, T. (2013) · Neuron · doi:10.1016/j.neuron.2012.12.030 · Cited by 41
Role of Homeostasis in Learning Sparse Representations
Perrinet, L. (2010) · Neural Computation · doi:10.1162/neco.2010.05-08-795 · Cited by 41
Kastner, D. B., & Baccus, S. A. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.11.012 · Cited by 41
Self-citationAuditory Mismatch Negativity Under Predictive Coding Framework and Its Role in Psychotic Disorders
Fong, C. Y., Law, W. H. C., Uka, T., & Koike, S. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.557932 · Cited by 48
Predictive Coding, Variational Autoencoders, and Biological Connections
Marino, J. (2021) · Neural Computation · doi:10.1162/neco_a_01458 · Cited by 51
Clark, A. (2014) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199832798.003.0002 · Cited by 41
Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual CortexPreprint
Homann, J., Koay, S. A., Glidden, A. M., Tank, D. W., & Berry, M. J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/197608 · Cited by 39
Barton, J. P., & Cocco, S. (2013) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2013/03/p03002 · Cited by 38
Distributed processing of color and form in the visual cortex
Rentzeperis, I., Nikolaev, A. R., Kiper, D., & van Leeuwen, C. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00932 · Cited by 44
A Retinal Circuit Generating a Dynamic Predictive Code for Oriented Features
Johnston, J., Seibel, S.-H., Darnet, L., Renninger, S. L., Orger, M. B., & Lagnado, L. (2019) · Neuron · doi:10.1016/j.neuron.2019.04.002 · Cited by 38
Evolving Local Plasticity Rules for Synergistic Learning in Echo State Networks
Wang, X., Jin, Y., & Hao, K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2919903 · Cited by 37
New Labels for Old Ideas: Predictive Processing and the Interpretation of Neural Signals
Cao, R. (2020) · Review of Philosophy and Psychology · doi:10.1007/s13164-020-00481-x · Cited by 38
Refractoriness about adaptation
O’Shea, R. P. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00038 · Cited by 36
Predictive coding of visual motion in both monocular and binocular human visual processing
van Heusden, E., Harris, A. M., Garrido, M. I., & Hogendoorn, H. (2019) · Journal of Vision · doi:10.1167/19.1.3 · Cited by 36
Population adaptation in efficient balanced networks
Gutierrez, G. J., & Denève, S. (2019) · eLife · doi:10.7554/elife.46926 · Cited by 35
Kumar, S., & Schönwiesner, M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3812-12.2012 · Cited by 35
Mixtures of strategies underlie rodent behavior during reversal learning
Le, N. M., Yıldırım, M., Wang, Y., 杉原, 弘., Jazayeri, M., & Sur, M. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011430 · Cited by 45
Cook, E. P., Guest, J. A., Liang, Y., Masse, N. Y., & Colbert, C. M. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00414.2007 · Cited by 34
Balakrishnan, V., Puthussery, T., Kim, M.-H., Taylor, W. R., & von Gersdorff, H. (2015) · Neuron · doi:10.1016/j.neuron.2015.07.016 · Cited by 34
A biologically inspired spiking model of visual processing for image feature detection
Kerr, D., McGinnity, T. M., Coleman, S., & Clogenson, M. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.011 · Cited by 34
Guo, T., Tsai, D., Bai, S., Morley, J. W., Suaning, G. J., Lovell, N. H., & Dokos, S. (2014) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2014011732 · Cited by 37
Intensity-invariant coding in the auditory system
Barbour, D. L. (2011) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.04.009 · Cited by 34
Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons
Burak, Y., Lewallen, S., & Sompolinsky, H. (2009) · Neural Computation · doi:10.1162/neco.2009.07-08-830 · Cited by 34
History-Dependent Odor Processing in the Mouse Olfactory Bulb
Vinograd, A., Livneh, Y., & Mizrahi, A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0755-17.2017 · Cited by 34
Beyond arousal: Prediction error related to aversive events promotes episodic memory formation.
Kalbe, F., & Schwabe, L. (2019) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000728 · Cited by 35
Achieving stable dynamics in neural circuits
Kozachkov, L., Lundqvist, M., Slotine, J.-J. E., & Miller, E. K. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007659 · Cited by 34
Role of homeostasis in learning sparse representations
Perrinet, L. (2010) · Cited by 33
Adaptation improves performance on a visual search task
Wissig, S., Patterson, C. A., & Kohn, A. (2013) · Journal of Vision · doi:10.1167/13.2.6 · Cited by 33
Park, I. M., & Pillow, J. W. (2026) · PLoS Computational Biology · doi:10.1101/178418 · Cited by 31
Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons
Yaeli, S. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00130 · Cited by 33
Orlandi, N. (2017) · Synthese · doi:10.1007/s11229-017-1373-4 · Cited by 35
Sederberg, A., MacLean, J. N., & Palmer, S. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710779115 · Cited by 31
Momentum-like effects and the dynamics of perception, cognition, and action
Hubbard, T. L. (2019) · Attention Perception & Psychophysics · doi:10.3758/s13414-019-01770-z · Cited by 35
Synaptic input to OFF parasol ganglion cells in macaque retina
Bordt, A. S., Hoshi, H., Yamada, E. S., Perryman, W. C., & MARSHAK, D. W. (2006) · The Journal of Comparative Neurology · doi:10.1002/cne.21040 · Cited by 31
Lane detection based on spiking neural network and hough transform
Li, X., Wu, Q., Yu, K., Hou, L., & Yang, H. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7407954 · Cited by 31
den Ouden, C., Zhou, A., Mepani, V., Kovács, G., Vogels, R., & Feuerriegel, D. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120347 · Cited by 36
Li, G., Vı́gh, J., & von Gersdorff, H. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.0410-07.2007 · Cited by 30
Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions
Price, B. H., & Gavornik, J. P. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.929348 · Cited by 32
Motion Adaptation Enhances Object-Induced Neural Activity in Three-Dimensional Virtual Environment
Liang, P., Kern, R., & Egelhaaf, M. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0203-08.2008 · Cited by 28
Primary Visual Cortex Represents the Difference Between Past and Present
Nortmann, N., Rekauzke, S., Onat, S., Peter König, B., & Jancke, D. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht318 · Cited by 30
Visual brain plasticity induced by central and peripheral visual field loss
Sanda, N., Cerliani, L., Authié, C., Sabbah, N., Sahel, J., Habas, C., Safran, A. B., & de Schotten, M. T. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1700-7 · Cited by 28
The principle of consistency and the cause and function of behaviour
KAMPEN, H. S. V. (2018) · Behavioural Processes · doi:10.1016/j.beproc.2018.12.013 · Cited by 34
Neural Reconstruction with Approximate Message Passing (NeuRAMP)
Fletcher, A. K., Rangan, S., Varshney, L. R., & Bhargava, A. (2011) · Cited by 27
Human Vision Reconstructs Time to Satisfy Causal Constraints
Bechlivanidis, C., Buehner, M. J., Tecwyn, E. C., Lagnado, D. A., Hoerl, C., & McCormack, T. (2022) · Psychological Science · doi:10.1177/09567976211032663 · Cited by 27
Sensory noise predicts divisive reshaping of receptive fields
Chalk, M., Masset, P., Denève, S., & Gutkin, B. S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005582 · Cited by 27
Adaptation of Inhibition Mediates Retinal Sensitization
Kastner, D. B., Ozuysal, Y., Panagiotakos, G., & Baccus, S. A. (2019) · Current Biology · doi:10.1016/j.cub.2019.06.081 · Cited by 26
Self-citationProcessing visual stimuli using hierarchical spiking neural networks
Wu, Q., McGinnity, T. M., Maguire, L., Belatreche, A., & Glackin, B. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.10.020 · Cited by 27
Kim, M.-H., & von Gersdorff, H. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.028 · Cited by 26
Information transmission with spiking Bayesian neurons
Lochmann, T., & Denève, S. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/5/055019 · Cited by 26
Nirenberg, S., Bomash, I., Pillow, J. W., & Victor, J. D. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00878.2009 · Cited by 25
Optimum spatiotemporal receptive fields for vision in dim light
Klaus, A., & Warrant, E. J. (2009) · Journal of Vision · doi:10.1167/9.4.18 · Cited by 25
Recurrent Antitopographic Inhibition Mediates Competitive Stimulus Selection in an Attention Network
Lai, D., Brandt, S. F., Luksch, H., & Weßel, R. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00673.2010 · Cited by 26
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