The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Bayesian inference with probabilistic population codes
Abstract
Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.
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
“The agreement between this simple model and the data affirms the plausibility of the “Bayesian sequential sampling” framework (41).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [41]
In the citing paper
“From this perspective, our neural recordings support the idea of a “Bayesian brain” (66) and a neural mechanism of decision making that does not flip into a fixed point or attractor state but instead approximates the formation of a probability distribution (41, 67).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [41]
In the citing paper
“It is probably also consistent with other models that exploit a broader population of LIP neurons to encode posterior probability (22, 41).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [41]
Citations
Cites
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844
Neural Information Processing Systems
(2018) · The MIT Press eBooks · doi:10.7551/mitpress/11474.003.0014 · Cited by 3,776
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
(1994) · Choice Reviews Online · doi:10.5860/choice.31-4365 · Cited by 1,897
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Neural correlates of decision variables in parietal cortex
Platt, M. L., & Glimcher, P. W. (1999) · Nature · doi:10.1038/22268 · Cited by 1,721
Perception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425
The statistical reliability of signals in single neurons in cat and monkey visual cortex
Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983) · Vision Research · doi:10.1016/0042-6989(83)90200-6 · Cited by 1,002
Neural computations that underlie decisions about sensory stimuli
Gold, J. I., & Shadlen, M. N. (2001) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01567-9 · Cited by 881
Flagged first, then by citations
Research works printed but not linked: 2
- Hinton, G.E. in Proceedings of the Ninth International Conference on Artificial Neural Network 1–6 (IEEE, London, England, 1999).
- Hoyer, P.O. & Hyvarinen, A. in Neural Information Processing Systems 277–284 (MIT Press, Cambridge, Massachusetts, 2003).
Cited by
Sorted by citations
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831
The cognitive neuroscience of ageing
Grady, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3256 · Cited by 1,286
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
When the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156
Changing concepts of working memory
Ma, W. J., Husain, M., & Bays, P. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3655 · Cited by 1,208
Self-citationKriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093
Causal Inference in Multisensory Perception
Körding, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000943 · Cited by 1,069
Self-citationRepresentation of Confidence Associated with a Decision by Neurons in the Parietal Cortex
Kiani, R., & Shadlen, M. N. (2009) · Science · doi:10.1126/science.1169405 · Cited by 1,027
Naselaris, T., Kay, K., Nishimoto, S., & Gallant, J. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.073 · Cited by 945
What is consciousness, and could machines have it?
Dehaene, S., Lau, H., & Kouider, S. (2017) · Science · doi:10.1126/science.aan8871 · Cited by 855
Extracting information from neuronal populations: information theory and decoding approaches
Quiroga, R. Q., & Panzeri, S. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2578 · Cited by 827
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology
McDonnell, M. D., & Abbott, D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000348 · Cited by 774
The benefits of noise in neural systems: bridging theory and experiment
McDonnell, M. D., & Ward, L. M. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn3061 · Cited by 794
Bahrami, B., Olsen, K., Latham, P. E., Roepstorff, A., Rees, G., & Frith, C. D. (2010) · Science · doi:10.1126/science.1185718 · Cited by 738
Self-citationBehavioral, Neural, and Computational Principles of Bodily Self-Consciousness
Blanke, O., Slater, M., & Serino, A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.029 · Cited by 698
Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672
Decision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671
Metacognition in human decision-making: confidence and error monitoring
Yeung, N., & Summerfield, C. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2011.0416 · Cited by 667
Probabilistic population codes for Bayesian decision making
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645
Self-citationNeural computations underlying arbitration between model-based and model-free learning
Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.028 · Cited by 642
Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel.
Kleinschmidt, D., & Jaeger, T. F. (2015) · Psychological Review · doi:10.1037/a0038695 · Cited by 684
Probabilistic brains: knowns and unknowns
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3495 · Cited by 615
“This implies that the code for the posterior distribution, p ( s | r ), can be deduced from the form of the neural variability, p ( r | s ) 47 .”
BackgroundInfluentialSelf-citationCardinal rules: visual orientation perception reflects knowledge of environmental statistics
Girshick, A. R., Landy, M. S., & Simoncelli, E. P. (2011) · Nature Neuroscience · doi:10.1038/nn.2831 · Cited by 613
Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions
Forstmann, B. U., Ratcliff, R., & Wagenmakers, E. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033645 · Cited by 665
Moment-to-moment brain signal variability: A next frontier in human brain mapping?
Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W. S., Lindenberger, U., McIntosh, A. R., & Grady, C. L. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.02.015 · Cited by 624
Probabilistic reasoning by neurons
Yang, T., & Shadlen, M. N. (2007) · Nature · doi:10.1038/nature05852 · Cited by 597
Decision theory, reinforcement learning, and the brain
Dayan, P., & Daw, N. D. (2008) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.8.4.429 · Cited by 580
The Cost of Accumulating Evidence in Perceptual Decision Making
Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4010-11.2012 · Cited by 577
Self-citationNeural correlates of multisensory cue integration in macaque MSTd
Gu, Y., Angelaki, D. E., & DeAngelis, G. C. (2008) · Nature Neuroscience · doi:10.1038/nn.2191 · Cited by 554
Brain plasticity through the life span: Learning to learn and action video games
Bavelier, D., Green, C. S., Pouget, A., & Schrater, P. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-152832 · Cited by 541
Self-citationJones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
Neural computations associated with goal-directed choice
Rangel, A., & Hare, T. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.03.001 · Cited by 532
Variability in encoding precision accounts for visual short-term memory limitations
van den Berg, R., Shin, H., Chou, W.-C., George, R. G., & Ma, W. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117465109 · Cited by 532
Self-citationSlow dynamics and high variability in balanced cortical networks with clustered connections
Litwin-Kumar, A., & Doiron, B. (2012) · Nature Neuroscience · doi:10.1038/nn.3220 · Cited by 555
Confidence and certainty: Distinct probabilistic quantities for different goals
Pouget, A., Drugowitsch, J., & Kepecs, A. (2016) · Nature Neuroscience · doi:10.1038/nn.4240 · Cited by 528
Self-citationGenerative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model
Breakspear, M. J., Heitmann, S., & Daffertshofer, A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00190 · Cited by 531
Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4761-07.2008 · Cited by 500
The Future of Memristors: Materials Engineering and Neural Networks
Sun, K., Chen, J., & Yan, X. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202006773 · Cited by 491
Posterior cingulate cortex: adapting behavior to a changing world
Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y., & Platt, M. L. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.02.002 · Cited by 504
Silver, R. A. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2864 · Cited by 481
Choice Certainty Is Informed by Both Evidence and Decision Time
Kiani, R., Corthell, L., & Shadlen, M. N. (2014) · Neuron · doi:10.1016/j.neuron.2014.12.015 · Cited by 470
The Importance of Being Variable
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5641-10.2011 · Cited by 484
Consensus Paper: The Role of the Cerebellum in Perceptual Processes
Baumann, O., Borra, R. J., Bower, J. M., Cullen, K. E., Habas, C., Ivry, R. B., Leggio, M., Mattingley, J. B., Molinari, M., Moulton, E. A., et al. (2015) · The Cerebellum · doi:10.1007/s12311-014-0627-7 · Cited by 462
Neural correlates of reliability-based cue weighting during multisensory integration
Fetsch, C. R., Pouget, A., DeAngelis, G. C., & Angelaki, D. E. (2012) · Nature Neuroscience · doi:10.1038/nn.2983 · Cited by 459
Self-citationFrank, M. J., Doll, B. B., Oas-Terpstra, J., & Moreno, F. (2009) · Nature Neuroscience · doi:10.1038/nn.2342 · Cited by 479
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436
Development of Cue Integration in Human Navigation
Nardini, M., Jones, P., Bedford, R., & Braddick, O. (2008) · Current Biology · doi:10.1016/j.cub.2008.04.021 · Cited by 431
Incubation, insight, and creative problem solving: A unified theory and a connectionist model.
Hélie, S., & Sun, R. (2010) · Psychological Review · doi:10.1037/a0019532 · Cited by 449
Dynamic Reweighting of Visual and Vestibular Cues during Self-Motion Perception
Fetsch, C. R., Turner, A. H., DeAngelis, G. C., & Angelaki, D. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2574-09.2009 · Cited by 436
Toward a neurobiology of delusions
Corlett, P. R., Taylor, J. R., Wang, X.-J., Fletcher, P. C., & Krystal, J. H. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2010.06.007 · Cited by 394
Perceptual Decision Making in Rodents, Monkeys, and Humans
Hanks, T. D., & Summerfield, C. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.003 · Cited by 409
Multisensory integration: psychophysics, neurophysiology, and computation
Angelaki, D. E., Gu, Y., & DeAngelis, G. C. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.06.008 · Cited by 383
Elapsed Decision Time Affects the Weighting of Prior Probability in a Perceptual Decision Task
Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E., & Shadlen, M. N. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.5613-10.2011 · Cited by 378
Decision-related activity in sensory neurons reflects more than a neuron’s causal effect
Nienborg, H., & Cumming, B. G. (2009) · Nature · doi:10.1038/nature07821 · Cited by 365
Correlations and Neuronal Population Information
Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013851 · Cited by 372
Self-citationImproved Probabilistic Inference as a General Learning Mechanism with Action Video Games
Green, C. S., Pouget, A., & Bavelier, D. (2010) · Current Biology · doi:10.1016/j.cub.2010.07.040 · Cited by 356
Self-citationConfidence as Bayesian probability: From neural origins to behavior
Meyniel, F., Sigman, M., & Mainen, Z. F. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.039 · Cited by 349
Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception
Rohe, T., & Noppeney, U. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002073 · Cited by 344
Baronchelli, A., Ferrer‐i‐Cancho, R., Pastor‐Satorras, R., Chater, N., & Christiansen, M. H. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.04.010 · Cited by 360
Suboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342
Vul, E., & Pashler, H. (2008) · Psychological Science · doi:10.1111/j.1467-9280.2008.02136.x · Cited by 333
Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning
Oaksford, M., & Chater, N. (2009) · Behavioral and Brain Sciences · doi:10.1017/s0140525x09000284 · Cited by 350
Circuits for Action and Cognition: A View from the Superior Colliculus
Basso, M. A., & May, P. J. (2017) · Annual Review of Vision Science · doi:10.1146/annurev-vision-102016-061234 · Cited by 347
A normalization model of multisensory integration
Ohshiro, T., Angelaki, D. E., & DeAngelis, G. C. (2011) · Nature Neuroscience · doi:10.1038/nn.2815 · Cited by 320
Estimating the sources of motor errors for adaptation and generalization
Berniker, M., & Kording, K. (2008) · Nature Neuroscience · doi:10.1038/nn.2229 · Cited by 316
Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.016 · Cited by 316
Self-citationDecision theory: what “should” the nervous system do?
Körding, K. (2007) · Science · doi:10.1126/science.1142998 · Cited by 313
Factorial comparison of working memory models.
van den Berg, R., Awh, E., & Ma, W. J. (2014) · Psychological Review · doi:10.1037/a0035234 · Cited by 311
Self-citationWith or without you: predictive coding and Bayesian inference in the brain
Aitchison, L., & Lengyel, M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.010 · Cited by 308
A computational framework for the study of confidence in humans and animals
Kepecs, A., & Mainen, Z. F. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0037 · Cited by 305
Serino, A. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.016 · Cited by 304
Fetsch, C. R., DeAngelis, G. C., & Angelaki, D. E. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3503 · Cited by 322
Behavior needs neural variability
Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.023 · Cited by 317
A tutorial introduction to Bayesian models of cognitive development
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011) · Cognition · doi:10.1016/j.cognition.2010.11.015 · Cited by 300
Neural Coding of Uncertainty and Probability
Ma, W. J., & Jazayeri, M. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014017 · Cited by 290
Self-citationNessler, B., Pfeiffer, M., Buesing, L., & Maass, W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003037 · Cited by 281
The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs055 · Cited by 276
Maÿe, A., Hsieh, C., Sugihara, G., & Brembs, B. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000443 · Cited by 280
Encoding and decoding in parietal cortex during sensorimotor decision-making
Park, I. M., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3800 · Cited by 273
Reliability, synchrony and noise
Ermentrout, B., Galán, R. F., & Urban, N. N. (2008) · Trends in Neurosciences · doi:10.1016/j.tins.2008.06.002 · Cited by 268
The practical and principled problems with educational neuroscience.
Bowers, J. S. (2016) · Psychological Review · doi:10.1037/rev0000025 · Cited by 258
Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex
Orbán, G., Berkes, P., Fiser, J., & Lengyel, M. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.038 · Cited by 275
Metacognition and Confidence: A Review and Synthesis
Fleming, S. M. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022423-032425 · Cited by 256
“…model, neurons encode parameters of probability distributions, with different neurons tuned to different stimulus features (such as its orientation or colour), such that a population of such neurons represents a probability distribution over features, given a sensory measurement (Ma et al., 2006).”
Trommershäuser, J., Kording, K. P., & Landy, M. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.001.0001 · Cited by 256
Fougnie, D. L., & Alvarez, G. A. (2011) · Journal of Vision · doi:10.1167/11.12.3 · Cited by 251
Restoring Latent Visual Working Memory Representations in Human Cortex
Sprague, T. C., Ester, E. F., & Serences, J. T. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.006 · Cited by 268
The Illusion of Owning a Third Arm
Guterstam, A., Petkova, V. I., & Ehrsson, H. H. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017208 · Cited by 262
Perceptual confidence neglects decision-incongruent evidence in the brain
Peters, M. A. K., Thesen, T., Ko, Y., Maniscalco, B., Carlson, C., Davidson, M., Doyle, W., Kuzniecky, R., Devinsky, O., Halgren, E., et al. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0139 · Cited by 265
Decoding the activity of neuronal populations in macaque primary visual cortex
Graf, A. B. A., Kohn, A., Jazayeri, M., & Movshon, J. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2733 · Cited by 254
The functional role of serial dependence
Cicchini, G. M., Mikellidou, K., & Burr, D. C. (2018) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2018.1722 · Cited by 262
Functional neuronal circuitry and oscillatory dynamics in human brain organoids
Sharf, T., van der Molen, T., Glasauer, S. M. K., Guzman, E., Buccino, A. P., Luna, G., Cheng, Z., Audouard, M., Ranasinghe, K. G., Kudo, K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32115-4 · Cited by 251
Relating neuronal firing patterns to functional differentiation of cerebral cortex.
Shinomoto, S., Kim, H., Shimokawa, T., Matsuno, N., Funahashi, S., Shima, K., Fujita, I., Tamura, H., Doi, T., Kawano, K., et al. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000433 · Cited by 239
Gershman, S. J., & Uchida, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0220-7 · Cited by 236
Spatially Global Representations in Human Primary Visual Cortex during Working Memory Maintenance
Ester, E. F., Serences, J. T., & Awh, E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.4388-09.2009 · Cited by 250
Beyond Poisson: Increased Spike-Time Regularity across Primate Parietal Cortex
Maimon, G., & Assad, J. A. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.021 · Cited by 230
Verstynen, T., & Sabes, P. N. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6525-10.2011 · Cited by 227
Metadata
- Authors
- Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A.
- Affiliations
- University of Rochester (US)
- National Hospital for Neurology and Neurosurgery (GB)
- Corresponding author
- Pouget, Alexandre
- Venue
- Nature Neuroscience
- Published
- 2006-10-22
- Volume
- 9
- Issue
- 11
- Pages
- 1432-1438
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2133877521
- Cited by
- 1,457
- References
- 49
- Citations by year
- 2026: 55 · 2025: 79 · 2024: 63 · 2023: 87 · 2022: 62 · 2021: 81 · 2020: 69 · 2019: 93 · 2018: 76 · 2017: 96 · 2016: 104 · 2015: 69 · 2014: 93 · 2013: 74 · 2012: 78
- FWCI
- 19.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH062447, T32MH019942, R01 MH62447, T32 MH19942)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- Bayesian inference, neural population codes, neural variability, shot noise, Bayes' theorem, cue integration, cortical circuits, neural coding
- MeSH terms
- Algorithms, Bayes Theorem, Cerebral Cortex, Humans, Models, Neurological, Nerve Net, Models, Statistical, Normal Distribution, Poisson Distribution
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
