Fitting predictive coding to the neurophysiological data
Open accessAbstract
Recent neurophysiological data showing the effects of locomotion on neural activity in mouse primary visual cortex has been interpreted as providing strong support for the predictive coding account of cortical function. Specifically, this work has been interpreted as providing direct evidence that prediction-error, a distinguishing property of predictive coding, is encoded in cortex. This article evaluates these claims and highlights some of the discrepancies between the proposed predictive coding model and the neuro-biology. Furthermore, it is shown that the model can be modified so as to fit the empirical data more successfully.
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It is currently not clear whether the inference networks of predictive coding and hierarchical filtering would place distinct requirements on implementation or make distinct predictions about neural readouts.
“While the message passing architecture implied by different predictive coding models has been examined in detail and partly matched with neuroanatomy and -physiology (for overviews, see Spratling, 2019; Keller & Mrsic-Flogel, 2018; Bastos et al., 2012; Shipp, 2016), and hierarchical filtering models share many similarities with predictive coding models, it is currently not clear whether the”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Spratling, 2019
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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 · cited 5× · see citing works
“…including the Bayesian brain hypothesis, analysis-by-synthesis, predictive coding, and the free-energy principle (Barlow, 1994; Chater et al., 2006; Clark, 2013; Friston, 2009, 2005; Griffiths et al., 2008; Griffiths and Tenenbaum, 2006; Kersten et al., 2004; Knill and Richards, 1996; Lee and…”
BackgroundMethodInfluentialRao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · see citing works
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284 · see citing works
Neuronal Population Coding of Movement Direction
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986) · Science · doi:10.1126/science.3749885 · Cited by 3,187 · see citing works
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 · see citing works
Hierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · cited 1× · see citing works
“This general idea has inspired more specific theories about how such inference is performed in the brain, including the Bayesian brain hypothesis, analysis-by-synthesis, predictive coding, and the free-energy principle (Barlow, 1994; Chater et al., 2006; Clark, 2013; Friston, 2009, 2005; Griffiths et al., 2008; Griffi…”
BackgroundPerception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425 · see citing works
Object perception as Bayesian inference
Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269 · see citing works
On the computational architecture of the neocortex: II The role of cortico-cortical loops
Mumford, D. (1992) · Biological Cybernetics · doi:10.1007/bf00198477 · Cited by 1,074 · see citing works
Expectation (and attention) in visual cognition
Summerfield, C., & Egner, T. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.06.003 · Cited by 950 · see citing works
On the computational architecture of the neocortex: I. The role of the thalamo-cortical loop
Mumford, D. (1991) · Biological Cybernetics · doi:10.1007/bf00202389 · Cited by 929 · see citing works
Vision as Bayesian inference: analysis by synthesis?
Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.002 · Cited by 849 · see citing works
Predictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836 · see citing works
Predictions not commands: Active inference in the motor system
Adams, R. A., Shipp, S., & Friston, K. J. (2013) · Brain Structure and Function · doi:10.1007/s00429-012-0475-5 · Cited by 765 · see citing works
Information processing with population codes
Pouget, A., Dayan, P., & Zemel, R. S. (2000) · Nature reviews. Neuroscience · doi:10.1038/35039062 · Cited by 744 · see citing works
Neural repetition suppression reflects fulfilled perceptual expectations
Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M.-M., & Egner, T. (2008) · Nature Neuroscience · doi:10.1038/nn.2163 · Cited by 692 · see citing works
Sensorimotor mismatch signals in primary visual cortex of the behaving mouse
Keller, G. B., Bonhoeffer, T., & Hübener, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.040 · Cited by 668 · see citing works
Action understanding and active inference
Friston, K., Mattout, J., & Kilner, J. (2011) · Biological Cybernetics · doi:10.1007/s00422-011-0424-z · Cited by 665 · see citing works
Prediction, cognition and the brain
Bubić, A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00025 · Cited by 658 · see citing works
Large-Scale Neuronal Theories of the Brain
Koch, C., Davis, J. L., Book, A., Churchland, P., Ramachandran, V., & Sejnowski, T. (1994) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(95)90097-7 · Cited by 656 · see citing works
Optimal Predictions in Everyday Cognition
Griffiths, T. L., & Tenenbaum, J. B. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01780.x · Cited by 637 · see citing works
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 · cited 2× · see citing works
“, Bayesian) multisensory integration (Spratling, 2016), consistent with psychophysiological experiments showing that human performance in sensory cue integration tasks is near optimal (Ernst and Jäkel, 2003; Ma and Pouget, 2008; Pouget et al., 2013; Seilheimer et al., 2014).”
BackgroundEvidence for a hierarchy of predictions and prediction errors in human cortex
Wacongne, C., Labyt, E., van Wassenhove, V., Bekinschtein, T., Naccache, L., & Dehaene, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117807108 · Cited by 560 · see citing works
Probabilistic models of cognition: Conceptual foundations
Chater, N., Tenenbaum, J. B., & Yuille, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.007 · Cited by 546 · see citing works
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.006 · Cited by 514 · see citing works
Shape perception reduces activity in human primary visual cortex
Murray, S. O., Kersten, D., Olshausen, B. A., Schrater, P., & Woods, D. L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.192579399 · Cited by 496 · see citing works
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule
Ercsey-Ravasz, M., Markov, N. T., Lamy, C., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.036 · Cited by 487 · see citing works
Huang, Y., & Rao, R. P. N. (2011) · WIREs Cognitive Science · doi:10.1002/wcs.142 · Cited by 470 · see citing works
A review of predictive coding algorithms
Spratling, M. W. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2015.11.003 · Cited by 437 · cited 4× · see citing works
“The configuration of the model shown in Figure 2c was simulated for a second time using the equations that define the PC/BC-DIM variation of predictive coding (Spratling, 2008a, 2017b).”
BackgroundMethodInfluentialSelf-citationEliaš, P. (1955) · IEEE Transactions on Information Theory · doi:10.1109/tit.1955.1055126 · Cited by 436 · see citing works
Stimulus Predictability Reduces Responses in Primary Visual Cortex
Alink, A., Schwiedrzik, C. M., Kohler, A., Singer, W., & Muckli, L. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3730-10.2010 · Cited by 435 · see citing works
Attention reverses the effect of prediction in silencing sensory signals
Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C., & de Lange, F. P. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr310 · Cited by 430 · see citing works
I NFERENCE AND C OMPUTATION WITH P OPULATION C ODES
Pouget, A., Dayan, P., & Zemel, R. S. (2003) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131112 · Cited by 429 · see citing works
Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?
Heilbron, M., & Chait, M. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2017.07.061 · Cited by 428 · see citing works
The graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415 · see citing works
Integration of visual motion and locomotion in mouse visual cortex
Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D., & Carandini, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3567 · Cited by 402 · see citing works
A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions
Leinweber, M., Ward, D. R., Sobczak, J. M., Attinger, A., & Keller, G. B. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.036 · Cited by 375 · see citing works
Efficient computation and cue integration with noisy population codes
Denève, S., Latham, P. E., & Pouget, A. (2001) · Nature Neuroscience · doi:10.1038/90541 · Cited by 351 · see citing works
Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream
Egner, T., Monti, J. M., & Summerfield, C. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.2770-10.2010 · Cited by 350 · see citing works
Neural elements for predictive coding
Shipp, S. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01792 · Cited by 340 · see citing works
Prior expectations induce prestimulus sensory templates
Kok, P., Mostert, P., & de Lange, F. P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705652114 · Cited by 340 · see citing works
Experience-dependent spatial expectations in mouse visual cortex
Fiser, A., Mahringer, D., Oyibo, H. K., Petersen, A. V., Leinweber, M., & Keller, G. B. (2016) · Nature Neuroscience · doi:10.1038/nn.4385 · Cited by 332 · see citing works
A Neural Representation of Prior Information during Perceptual Inference
Summerfield, C., & Koechlin, É. (2008) · Neuron · doi:10.1016/j.neuron.2008.05.021 · Cited by 300 · see citing works
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 · see citing works
Prior expectations bias sensory representations in visual cortex
Kok, P., Brouwer, G. J., van Gerven, M. A. J., & de Lange, F. P. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.0742-13.2013 · Cited by 260 · see citing works
Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex
Attinger, A., Wang, B., & Keller, G. B. (2017) · Cell · doi:10.1016/j.cell.2017.05.023 · Cited by 252 · see citing works
Predictive coding as a model of biased competition in visual attention
Spratling, M. (2008) · Vision Research · doi:10.1016/j.visres.2008.03.009 · Cited by 240 · see citing works
Self-citationCoherent encoding of subjective spatial position in visual cortex and hippocampus
Saleem, A. B., Diamanti, E. M., Fournier, J., Harris, K. D., & Carandini, M. (2018) · Nature · doi:10.1038/s41586-018-0516-1 · Cited by 239 · see citing works
An Introduction to Model-Based Cognitive Neuroscience
Forstmann, B. U., & Wagenmakers, E. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2236-9 · Cited by 228 · see citing works
Bayesian models: the structure of the world, uncertainty, behavior, and the brain
Vilares, I., & Kording, K. P. (2011) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.05965.x · Cited by 214 · see citing works
Global optimization of cerebral cortex layout
Cherniak, C., Mokhtarzada, Z., Rodriguez‐Esteban, R., & Changizi, K. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0305212101 · Cited by 214 · see citing works
Mismatch Receptive Fields in Mouse Visual Cortex
Zmarz, P., & Keller, G. B. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.057 · Cited by 178 · see citing works
Organizing probabilistic models of perception
Ma, W. J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.010 · Cited by 167 · see citing works
Reconciling predictive coding and biased competition models of cortical function
Spratling, M. W. (2008) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.004.2008 · Cited by 153 · see citing works
Self-citationPerceptual grouping and the interactions between visual cortical areas
Murray, S. O., Schrater, P., & Kersten, D. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.03.010 · Cited by 149 · see citing works
Perceptual grouping and inverse fMRI activity patterns in human visual cortex
Fang, F., Kersten, D., & Murray, S. O. (2008) · Journal of Vision · doi:10.1167/8.7.2 · Cited by 122 · see citing works
What is the computational goal of the neocortex
Barlow, H. B. (1994) · Cited by 121 · see citing works
Associative Prediction of Visual Shape in the Hippocampus
Kok, P., & Turk‐Browne, N. B. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0163-18.2018 · Cited by 112 · see citing works
Models and processes of multisensory cue combination
Seilheimer, R. L., Rosenberg, A., & Angelaki, D. E. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.11.008 · Cited by 105 · see citing works
Predictive Coding in Sensory Cortex
Kok, P., & de Lange, F. P. (2015) · An Introduction to Model-Based Cognitive Neuroscience · doi:10.1007/978-1-4939-2236-9_11 · Cited by 98 · see citing works
Linking neurons to behavior in multisensory perception: A computational review
Ma, W. J., & Pouget, A. (2008) · Brain Research · doi:10.1016/j.brainres.2008.04.082 · Cited by 84 · see citing works
Contextual signals in visual cortex
Khan, A. G., & Hofer, S. B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.05.003 · Cited by 81 · see citing works
Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation
Spratling, M., Meyer, K. D., & Kompass, R. (2009) · Computational Intelligence and Neuroscience · doi:10.1155/2009/381457 · Cited by 78 · see citing works
Self-citationAction and learning shape the activity of neuronal circuits in the visual cortex
Pakan, J. M. P., Francioni, V., & Rochefort, N. L. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.020 · Cited by 75 · see citing works
Image Segmentation Using a Sparse Coding Model of Cortical Area V1
Spratling, M. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2235850 · Cited by 71 · see citing works
Self-citationA Hierarchical Predictive Coding Model of Object Recognition in Natural Images
Spratling, M. (2016) · Cognitive Computation · doi:10.1007/s12559-016-9445-1 · Cited by 65 · see citing works
Self-citationPredictive feedback to V1 dynamically updates with sensory input
Edwards, G., Vetter, P., McGruer, F., Petro, L. S., & Muckli, L. F. (2017) · Scientific Reports · doi:10.1038/s41598-017-16093-y · Cited by 54 · see citing works
Predictive coding for motion stimuli in human early visual cortex
Schellekens, W., Wezel, R. J. A. V., Petridou, N., Ramsey, N. F., & Raemaekers, M. A. H. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0942-2 · Cited by 38 · see citing works
A neural implementation of Bayesian inference based on predictive coding
Spratling, M. (2016) · Connection Science · doi:10.1080/09540091.2016.1243655 · Cited by 30 · see citing works
Self-citationThe influence of locomotion on sensory processing and its underlying neuronal circuits
Busse, L. (2018) · e-Neuroforum · doi:10.1515/nf-2017-a046 · Cited by 23 · see citing works
Symbolic neural networks for cognitive capacities
Achler, T. (2014) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2014.07.001 · Cited by 15 · see citing works
Learning to Combine Arbitrary Signals from Vision and Touch
Jäkel, F., & Ernst, M. O. (2003) · TUbilio (Technical University of Darmstadt) · Cited by 8 · see citing works
Cortical Processing: How Mice Predict the Visual Effects of Locomotion
Fridman, M. V., & Petreanu, L. (2017) · Current Biology · doi:10.1016/j.cub.2017.10.038 · Cited by 5 · see citing works
Pattern Discovery for High-Dimensional Binary Datasets
Snåšel, V., Moravec, P., Húsek, D., Frolov, A., Řezanková, H., & Polyakov, P. Y. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69158-7_89 · Cited by 3 · see citing works
Learning to Combine Arbitrary Signals from Vision and Touch
Ernst, M. O., & Jäkel, F. (2003) · MPG.PuRe (Max Planck Society) · Cited by 1 · see citing works
Flagged first, then by citations
2 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Harpur, G. F. (1997). Low Entropy Coding with Unsupervised Neural Networks . PhD thesis, Department of Engineering, University of Cambridge.
- Pouget, A., Dayan, P., and Zemel, R. S. (2003). Inference and computation with population codes. Annual Review of Neuroscience , 26:381-410.
- Sn´ aˇ sel, V ., Moravec, P., H´ usek, D., Frolov, A., ˇ Rezankov´ a, H., and Polyakov, P. (2008). Pattern discovery for highdimensional binary datasets. In Proceedings of the International Conference on Neural Information Processing , volume 4984 of Lecture Notes in Computer Science , pages 861-72. Springer.
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- Authors
- Spratling, M. W.
- Affiliations
- King's College London (GB)
- Corresponding author
- Spratling, M.W.
- ORCID
- Spratling, M.W.
- Venue
- Brain Research
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 1720
- Pages
- 146313
- ISSN
- 0006-8993 (ISSN-L)
- OpenAlex
- W2955990109
- Cited by
- 23
- References
- 78
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 4 · 2023: 5 · 2022: 2 · 2021: 4 · 2020: 3
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- Locations
- Brain Research (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- predictive coding, prediction error, primary visual cortex, locomotion, cortical activity, mouse visual cortex, model fitting, neural coding
- MeSH terms
- Animals, Computer Simulation, Models, Neurological, Neurons, Neurophysiology, Software, Visual Cortex, Visual Perception, Mice, Renshaw Cells
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