Predictive Processing: A Canonical Cortical Computation
Open accessAbstract
This perspective describes predictive processing as a computational framework for understanding cortical function in the context of emerging evidence, with a focus on sensory processing. We discuss how the predictive processing framework may be implemented at the level of cortical circuits and how its implementation could be falsified experimentally. Lastly, we summarize the general implications of predictive processing on cortical function in healthy and diseased states.
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The GNW theory can be combined with Bayesian inference, in which GNW neurons provide a compressed high-level model of the external world, top-down signals broadcast predictions, and bottom-up signals convey sensory input and either amplify predicted signals or measure prediction error, allowing the central model to be updated and achieving a coherent multisensory model.
“GNW neurons, lying at the top of a deep feedforward network, provide a compressed, high-level symbolic model of an aspect of the external world; top-down signals broadcast the predictions that this model makes to lower-level areas; and bottom-up signals convey sensory signals and may either amplify the signals predicted by top-down input”
From Conscious Processing and the Global Neuronal Workspace Hypothesis (2020) · cites this paper as Keller and Mrsic-Flogel, 2018
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredCritiques of the standard implementation of PP, focusing on the model's biological plausibility, have spawned numerous alternative versions with revised circuitries and diverging predictions.
“Indeed, critiques of the standard implementation of PP, 18 focusing on the model's biological plausibility (e.g., neurons with both positive and negative firing rates), have spawned numerous alternative versions with revised circuitries and diverging predictions.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [41]
The section “Prediction error neurons and internal representation neurons” says that low baseline firing rates in cortical neurons make bidirectional error signalling less plausible, and proposes separate positive- and negative-error circuits. The Introduction acknowledges different predictive-processing variants. Our copy does not establish that critiques spawned numerous revised circuit models with diverging predictions. (LLM, conf=0.83)
Experimental — accuracy not yet measuredMethodological advances in high-resolution fMRI, optogenetics, calcium imaging, and serial single-unit recordings at multiple levels of the processing hierarchy are providing powerful new opportunities to trace neural markers of hierarchical PP dynamics.
“Although this represents a considerable challenge using conventional paradigms and neurophysiological techniques, methodological advances in high‐resolution fMRI, optogenetics, calcium imaging, and serial single‐unit recordings at multiple levels of the processing hierarchy are providing powerful new opportunities to trace neural markers of hierarchical PP dynamics.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [41]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredOmission responses do not appear easy to reconcile with feedforward models devoid of predictive influence on sensory processing.
“Although PP can comfortably accommodate prediction error signals being elicited in the absence of any sensory input, such omission responses do not appear easy to reconcile with feedforward models that are devoid of any predictive influence on sensory processing 41 (but see Ref. 50).”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [41]
Top-down influences are increasingly recognized as an integral component in early sensory processing.
“Top‐down influences are increasingly recognized as an integral component in early sensory processing, 41 , 128 , 184 , 185 , 186 , 187 but the contention that these hierarchical dynamics represent prediction error minimization has rarely been explicitly tested.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [41]
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 Keller & Mrsic-Flogel, 2018
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Cites
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
O'Keefe, J., & Dostrovsky, J. (1971) · Brain Research · doi:10.1016/0006-8993(71)90358-1 · Cited by 6,151 · see citing works
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 1× · see citing works
“Predictive processing as a conceptual framework for understanding brain has a long tradition in the fields of computational and cognitive neuroscience, and has been elegantly summarized elsewhere (Clark, 2013; Koster-Hale and Saxe, 2013).”
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 3× · see citing works
“The idea that the brain uses an internal model to predict sensory input based on movements and past sensory experience has been formalized in several different variants: predictive coding, hierarchical temporal memory, and Bayesian inference (Friston, 2005; Hawkins and Blakeslee, 2004; Körding and Wolpert, 2004; Rao a…”
Receptive fields of single neurones in the cat's striate cortex
Hubel, D. H., & Wiesel, T. N. (1959) · The Journal of Physiology · doi:10.1113/jphysiol.1959.sp006308 · Cited by 4,653 · 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
Microstructure of a spatial map in the entorhinal cortex
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005) · Nature · doi:10.1038/nature03721 · Cited by 4,068 · see citing works
An internal model for sensorimotor integration
Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995) · Science · doi:10.1126/science.7569931 · Cited by 3,553 · see citing works
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,412 · see citing works
Neurophysiological mechanisms underlying the understanding and imitation of action
Rizzolatti, G., Fogassi, L., & Gallese, V. (2001) · Nature Reviews Neuroscience · doi:10.1038/35090060 · Cited by 3,302 · see citing works
Gorillas in our midst: sustained inattentional blindness for dynamic events
Simons, D. J., & Chabris, C. F. (1999) · Perception · doi:10.1068/p281059 · Cited by 3,181 · see citing works
(1961) · Information and Control · doi:10.1016/s0019-9958(61)80064-8 · Cited by 2,904 · see citing works
Das Reafferenzprinzip: Wechselwirkungen zwischen Zentralnervensystem und Peripherie
von Holst, E., & Mittelstaedt, H. (1950) · Naturwissenschaften · doi:10.1007/bf00622503 · Cited by 2,655 · see citing works
RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT
Hubel, D. H., & Wiesel, T. N. (1965) · Journal of Neurophysiology · doi:10.1152/jn.1965.28.2.229 · Cited by 2,615 · see citing works
A mesoscale connectome of the mouse brain
Oh, S. W., Harris, J. A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A. M., et al. (2014) · Nature · doi:10.1038/nature13186 · Cited by 2,612 · see citing works
Dalmau, J., Tüzün, E., Wu, H., Masjuan, J., Rossi, J. E., Voloschin, A., Baehring, J. M., Shimazaki, H., Koide, R., King, D., et al. (2007) · Annals of Neurology · doi:10.1002/ana.21050 · Cited by 2,492 · see citing works
Internal models in the cerebellum
Wolpert, D. M., Miall, R. C., & Kawato, M. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01221-2 · Cited by 2,469 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 1× · see citing works
“It is possible that internal representation neurons are intermixed with prediction error neurons in different cortical layers, or that they are enriched in deep layers of cortex (Bastos et al., 2012), which are the main source of topdown signals (Felleman and Van Essen, 1991; Markov et al.”
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004 · see citing works
Invariant visual representation by single neurons in the human brain
Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005) · Nature · doi:10.1038/nature03687 · Cited by 1,988 · see citing works
Craik, K. J. W. (1943) · Cited by 1,845 · see citing works
Neural basis of the spontaneous optokinetic response produced by visual inversion
Sperry, R. W. (1950) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0055479 · Cited by 1,789 · see citing works
Uncertainty, Neuromodulation, and Attention
Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774 · see citing works
Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S., & Chklovskii, D. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030068 · Cited by 1,512 · see citing works
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483 · see citing works
Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia
Fletcher, P. C., & Frith, C. D. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2536 · Cited by 1,462 · see citing works
Forward models: supervised learning with a distal teacher
Jordan, M. I., & Rumelhart, D. E. (1992) · Cognitive Science · doi:10.1207/s15516709cog1603_1 · Cited by 1,451 · see citing works
Visual neurones responsive to faces in the monkey temporal cortex
Perrett, D. I., Rolls, E. T., & Caan, W. (1982) · Experimental Brain Research · doi:10.1007/bf00239352 · Cited by 1,386 · see citing works
Possible Principles Underlying the Transformations of Sensory Messages
Barlow, H. B. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/9780262518420.003.0013 · Cited by 1,318 · see citing works
The neocortical circuit: themes and variations
Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215 · see citing works
Cortical Map Reorganization Enabled by Nucleus Basalis Activity
Kilgard, M. P., & Merzenich, M. M. (1998) · Science · doi:10.1126/science.279.5357.1714 · Cited by 1,197 · see citing works
(2015) · Cited by 1,141 · see citing works
Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2018) · Science · doi:10.1126/science.aad6469 · Cited by 1,095 · see citing works
Highly selective receptive fields in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0623-08.2008 · Cited by 1,081 · see citing works
Processing of low-probability sounds by cortical neurons
Ulanovsky, N., Las, L., & Nelken, I. (2003) · Nature Neuroscience · doi:10.1038/nn1032 · Cited by 1,049 · see citing works
Hawkins, J., & Blakeslee, S. (2004) · Cited by 1,012 · see citing works
THE RESTORATION OF MOTOR FUNCTION FOLLOWING HEMIPLEGIA IN MAN
Twitchell, T. E. (1951) · Brain · doi:10.1093/brain/74.4.443 · Cited by 942 · see citing works
A Cortical Circuit for Gain Control by Behavioral State
Fu, Y., Tucciarone, J. M., Espinosa, J. S., Sheng, N., Darcy, D. P., Nicoll, R. A., Huang, Z. J., & Stryker, M. P. (2014) · Cell · doi:10.1016/j.cell.2014.01.050 · Cited by 937 · cited 2× · see citing works
“, 2015) is thought to be the consequence of neuromodulatory inputs (Fu et al., 2014; Polack et al., 2013), which exert context dependent influence on responses in visual cortex (Pakan et al.”
Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex
Reimer, J., McGinley, M. J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D. A., & Tolias, A. S. (2016) · Nature Communications · doi:10.1038/ncomms13289 · Cited by 926 · see citing works
Summation and inhibition in the frog's retina
Barlow, H. B. (1953) · The Journal of Physiology · doi:10.1113/jphysiol.1953.sp004829 · Cited by 892 · see citing works
Functional specificity of local synaptic connections in neocortical networks
Ko, H., Hofer, S. B., Pichler, B., Buchanan, K. A., Sjöström, P. J., & Mrsic‐Flogel, T. D. (2011) · Nature · doi:10.1038/nature09880 · Cited by 861 · see citing works
Self-citationKonishi, M. (1965) · Zeitschrift für Tierpsychologie · doi:10.1111/j.1439-0310.1965.tb01688.x · Cited by 832 · see citing works
Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding
Vinck, M., Batista-Brito, R., Knoblich, U., & Cardin, J. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.028 · Cited by 819 · see citing works
Thalamus plays a central role in ongoing cortical functioning
Sherman, S. M. (2016) · Nature Neuroscience · doi:10.1038/nn.4269 · Cited by 809 · see citing works
Long-range and local circuits for top-down modulation of visual cortex processing
Zhang, S., Xu, M., Kamigaki, T., Hoang Do, J. P., Chang, W.-C., Jenvay, S., Miyamichi, K., Luo, L., & Dan, Y. (2014) · Science · doi:10.1126/science.1254126 · Cited by 804 · see citing works
Neural Networks of the Mouse Neocortex
Zingg, B., Hintiryan, H., Gou, L., Song, M. Y., Bay, M., Bienkowski, M. S., Foster, N. N., Yamashita, S., Bowman, I., Toga, A. W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.02.023 · Cited by 797 · see citing works
Gregory, R. L. (1980) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1980.0090 · Cited by 789 · see citing works
Explaining the symptoms of schizophrenia: abnormalities in the awareness of action.
Frith, C. (2000) · Brain Research Reviews · doi:10.1016/s0165-0173(99)00052-1 · Cited by 782 · see citing works
Sensorimotor Adaptation in Speech Production
Houde, J. F., & Jordan, M. I. (1998) · Science · doi:10.1126/science.279.5354.1213 · Cited by 770 · see citing works
Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex
Markov, N. T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., et al. (2014) · Journal of Comparative Neurology · doi:10.1002/cne.23458 · Cited by 759 · see citing works
A disinhibitory circuit mediates motor integration in the somatosensory cortex
Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G., & Rudy, B. (2013) · Nature Neuroscience · doi:10.1038/nn.3544 · Cited by 758 · see citing works
A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex
Larkum, M. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2012.11.006 · Cited by 755 · see citing works
A synaptic organizing principle for cortical neuronal groups
Perin, R., Berger, T. K., & Markram, H. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016051108 · Cited by 718 · see citing works
A neural circuit for spatial summation in visual cortex
Adesnik, H., Bruns, W., Taniguchi, H., Huang, Z. J., & Scanziani, M. (2012) · Nature · doi:10.1038/nature11526 · Cited by 702 · 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
Self-citationThalamic amplification of cortical connectivity sustains attentional control
Schmitt, L. I., Wimmer, R. D., Nakajima, M., Happ, M., Mofakham, S., & Halassa, M. M. (2017) · Nature · doi:10.1038/nature22073 · Cited by 663 · see citing works
Cortical connectivity and sensory coding
Harris, K. D., & Mrsic‐Flogel, T. D. (2013) · Nature · doi:10.1038/nature12654 · Cited by 642 · see citing works
Self-citationAn aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641 · see citing works
Cellular mechanisms of brain state-dependent gain modulation in visual cortex
Polack, P.-O., Friedman, J., & Golshani, P. (2013) · Nature Neuroscience · doi:10.1038/nn.3464 · Cited by 628 · see citing works
Specific long-term memory traces in primary auditory cortex
Weinberger, N. M. (2004) · Nature reviews. Neuroscience · doi:10.1038/nrn1366 · Cited by 600 · see citing works
A synaptic and circuit basis for corollary discharge in the auditory cortex
Schneider, D. M., Nelson, A., & Mooney, R. (2014) · Nature · doi:10.1038/nature13724 · Cited by 580 · see citing works
Laminar Structure of Spontaneous and Sensory-Evoked Population Activity in Auditory Cortex
Sakata, S., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.020 · Cited by 580 · see citing works
Autism as a disorder of prediction
Sinha, P., Kjelgaard, M. M., Gandhi, T. K., Tsourides, K., Cardinaux, A. L., Pantazis, D., Diamond, S. P., & Held, R. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1416797111 · Cited by 563 · cited 1× · see citing works
“has been proposed as one of the attributes of autism (Lawson et al., 2014, 2017; Sinha et al., 2014).”
Packer, A. M., Russell, L. E., Dalgleish, H. W. P., & Häusser, M. (2014) · Nature Methods · doi:10.1038/nmeth.3217 · Cited by 553 · see citing works
Motor Cortex Is Required for Learning but Not for Executing a Motor Skill
Kawai, R., Markman, T. M., Poddar, R., Ko, R., Fantana, A. L., Dhawale, A. K., Kampff, A. R., & Ölveczky, B. P. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.024 · Cited by 551 · see citing works
Inhibition dominates sensory responses in the awake cortex
Haider, B., Häusser, M., & Carandini, M. (2012) · Nature · doi:10.1038/nature11665 · Cited by 541 · see citing works
Thalamic control of sensory selection in divided attention
Wimmer, R. D., Schmitt, L. I., Davidson, T. J., Nakajima, M., Deisseroth, K., & Halassa, M. M. (2015) · Nature · doi:10.1038/nature15398 · Cited by 530 · see citing works
Fast modulation of visual perception by basal forebrain cholinergic neurons
Pinto, L., Goard, M. J., Estandian, D., Xu, M., Kwan, A. C., Lee, S.-H., Harrison, T. C., Feng, G., & Dan, Y. (2013) · Nature Neuroscience · doi:10.1038/nn.3552 · Cited by 529 · see citing works
Functional organization of excitatory synaptic strength in primary visual cortex
Cossell, L., Iacaruso, M. F., Muir, D. R., Houlton, R., Sader, E., Ko, H., Hofer, S. B., & Mrsic‐Flogel, T. D. (2015) · Nature · doi:10.1038/nature14182 · Cited by 529 · see citing works
Self-citationMaintenance of persistent activity in a frontal thalamocortical loop
Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R., & Svoboda, K. (2017) · Nature · doi:10.1038/nature22324 · Cited by 529 · see citing works
Long-Range Neuronal Circuits Underlying the Interaction between Sensory and Motor Cortex
Mao, T., Kusefoglu, D., Hooks, B. M., Huber, D., Petreanu, L., & Svoboda, K. (2011) · Neuron · doi:10.1016/j.neuron.2011.07.029 · Cited by 514 · see citing works
A canonical microcircuit for neocortex
Douglas, R. J., Martin, K. A. C., & Whitteridge, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.480 · Cited by 514 · see citing works
Theory of Mind: A Neural Prediction Problem
Koster-Hale, J., & Saxe, R. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.020 · Cited by 513 · see citing works
FOSTER, D. H. (2010) · Vision Research · doi:10.1016/j.visres.2010.09.006 · Cited by 510 · see citing works
Robust neuronal dynamics in premotor cortex during motor planning
Li, N., Daie, K., Svoboda, K., & Druckmann, S. (2016) · Nature · doi:10.1038/nature17643 · Cited by 492 · cited 1× · see citing works
“A number of plausible mechanisms have been proposed for the persistence of neural activity, including strong and selective recurrent excitation between coactive neuronal assemblies within the cortex (Cossell et al., 2015; Li et al., 2016; Perin et al., 2011; Song et al., 2005), or via thalamocortical loops (Guo et al.”
NMDA Receptor-Dependent Ocular Dominance Plasticity in Adult Visual Cortex
Sawtell, N. B., Frenkel, M. Y., Philpot, B. D., Nakazawa, K., Tonegawa, S., & Bear, M. F. (2003) · Neuron · doi:10.1016/s0896-6273(03)00323-4 · Cited by 482 · see citing works
Ebner, M. (2007) · Cited by 480 · see citing works
Neural substrates of vocalization feedback monitoring in primate auditory cortex
Eliades, S. J., & Wang, X. (2008) · Nature · doi:10.1038/nature06910 · Cited by 479 · see citing works
Adults with autism overestimate the volatility of the sensory environment
Lawson, R. P., Mathys, C., & Rees, G. (2017) · Nature Neuroscience · doi:10.1038/nn.4615 · Cited by 470 · see citing works
The emergence of functional microcircuits in visual cortex
Ko, H., Cossell, L., Baragli, C., Antolík, J., Claudia Clopath, A., Hofer, S. B., & Mrsic‐Flogel, T. D. (2013) · Nature · doi:10.1038/nature12015 · Cited by 438 · see citing works
Self-citationA review of predictive coding algorithms
Spratling, M. W. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2015.11.003 · Cited by 437 · cited 2× · see citing works
“Note, we do not wish to diminish the importance of the discrepancies between the different theories we are grouping here (see e.g. (Spratling, 2017) for a review of different variants of predictive coding), but will focus on their common premise.”
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
Surfing uncertainty: prediction, action and the embodied mind
Bruineberg, J. P., & Froese, T. (2016) · International Journal of Performance Arts and Digital Media · doi:10.1080/14794713.2016.1257478 · Cited by 400 · see citing works
Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex
Poort, J., Khan, A. G., Pachitariu, M., Nemri, A., Oršolić, I., Krupic, J., Bauža, M., Sahani, M., Keller, G. B., Mrsic‐Flogel, T. D., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.037 · Cited by 397 · see citing works
Self-citationA 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
Self-citationA Tonic Hyperpolarization Underlying Contrast Adaptation in Cat Visual Cortex
Carandini, M., & Ferster, D. (1997) · Science · doi:10.1126/science.276.5314.949 · Cited by 372 · see citing works
From drugs to deprivation: A Bayesian framework for understanding models of psychosis
Corlett, P. R., Frith, C. D., & Fletcher, P. C. (2009) · Psychopharmacology · doi:10.1007/s00213-009-1561-0 · Cited by 367 · cited 1× · see citing works
“This would likely resemble positive symptoms of schizophrenia, as has been argued previously (Corlett et al., 2009; Fletcher and Frith, 2009; Frith, 2000).”
A Top-Down Cortical Circuit for Accurate Sensory Perception
Manita, S., Suzuki, T., Homma, C., Matsumoto, T., Odagawa, M., Yamada, K., Ota, K., Matsubara, C., Inutsuka, A., Sato, M., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.006 · Cited by 365 · see citing works
Synaptic Scaling and Homeostatic Plasticity in the Mouse Visual Cortex In Vivo
Keck, T., Keller, G. B., Jacobsen, R. I., Eysel, U. T., Bonhoeffer, T., & Hübener, M. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.018 · Cited by 357 · see citing works
Self-citationThalamic nuclei convey diverse contextual information to layer 1 of visual cortex
Roth, M., Dahmen, J. C., Muir, D. R., Imhof, F., Martini, F. J., & Hofer, S. B. (2015) · Nature Neuroscience · doi:10.1038/nn.4197 · Cited by 356 · 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
Thiele, A., & Bellgrove, M. A. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.008 · Cited by 350 · see citing works
de Kock, C. P. J., Bruno, R. M., Spors, H., & Sakmann, B. J. (2007) · The Journal of Physiology · doi:10.1113/jphysiol.2006.124321 · Cited by 344 · see citing works
Neural Correlate of Perceptual Adaptation to Gratings
Maffei, L., Fiorentini, A., & Bisti, S. (1973) · Science · doi:10.1126/science.182.4116.1036 · Cited by 338 · 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
Self-citationStatistical learning of visual transitions in monkey inferotemporal cortex
Meyer, T., & Olson, C. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1112895108 · Cited by 325 · see citing works
Learning enhances the relative impact of top-down processing in the visual cortex
Makino, H., & Komiyama, T. (2015) · Nature Neuroscience · doi:10.1038/nn.4061 · Cited by 318 · see citing works
Pakan, J. M., Lowe, S. C., Dylda, E., Keemink, S. W., Currie, S. P., Coutts, C. A., & Rochefort, N. L. (2016) · eLife · doi:10.7554/elife.14985 · Cited by 282 · cited 1× · see citing works
“, 2013), which exert context dependent influence on responses in visual cortex (Pakan et al., 2016).”
Comparative genomics of autism and schizophrenia
Crespi, B., Stead, P., & Elliot, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906080106 · Cited by 262 · see citing works
Circuits and Mechanisms for Surround Modulation in Visual Cortex
Angelucci, A., Bijanzadeh, M., Nurminen, L., Federer, F., Merlin, S., & Bressloff, P. C. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031418 · Cited by 256 · 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
Self-citationControl of synaptic plasticity in deep cortical networks
Roelfsema, P. R., & Holtmaat, A. J. D. G. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.6 · Cited by 245 · see citing works
Neural processing of auditory feedback during vocal practice in a songbird
Keller, G. B., & Hahnloser, R. H. R. (2009) · Nature · doi:10.1038/nature07467 · Cited by 243 · see citing works
Self-citationGating and control of primary visual cortex by pulvinar
Purushothaman, G., Marion, R., Li, K., & Casagrande, V. A. (2012) · Nature Neuroscience · doi:10.1038/nn.3106 · Cited by 236 · see citing works
Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex
Martins, A. R. O., & Froemke, R. C. (2015) · Nature Neuroscience · doi:10.1038/nn.4090 · Cited by 233 · see citing works
Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion
Brooks, J. X., Carriot, J., & Cullen, K. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4077 · Cited by 231 · see citing works
Learned spatiotemporal sequence recognition and prediction in primary visual cortex
Gavornik, J. P., & Bear, M. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3683 · Cited by 227 · see citing works
Heeger, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619788114 · Cited by 220 · see citing works
Shared Molecular Neuropathology Across Major Psychiatric Disorders Parallels Polygenic Overlap
Gandal, M. J., Haney, J. R., Parikshak, N., Leppä, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2019) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.17103 · Cited by 209 · see citing works
Predictive coding as a model of response properties in cortical area V1
Spratling, M. W. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.4911-09.2010 · Cited by 206 · see citing works
Gămănuț, R., Kennedy, H., Toroczkai, Z., Ercsey-Ravasz, M., Essen, D. C. V., Knoblauch, K., & Burkhalter, A. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.037 · Cited by 202 · see citing works
Late-onset anti–NMDA receptor encephalitis
Titulaer, M. J., McCracken, L., Gabilondo, I., Iizuka, T., Kawachi, I., Bataller, L., Torrents, A., Rosenfeld, M. R., Balice‐Gordon, R. J., Graus, F., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e3182a4a49c · Cited by 199 · see citing works
Activity recall in a visual cortical ensemble
Xu, S., Jiang, W.-C., Poo, M., & Dan, Y. (2012) · Nature Neuroscience · doi:10.1038/nn.3036 · Cited by 181 · see citing works
Lütcke, H. (2010) · Frontiers in Neural Circuits · doi:10.3389/fncir.2010.00009 · Cited by 178 · 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
Self-citationAttention and primary visual cortex
Posner, M. I., & Gilbert, C. D. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.2585 · Cited by 177 · see citing works
Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex
Miri, A., Warriner, C. L., Seely, J., Elsayed, G. F., Cunningham, J. P., Churchland, M. M., & Jessell, T. M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.042 · Cited by 175 · see citing works
Distinct recurrent versus afferent dynamics in cortical visual processing
Reinhold, K., Lien, A. D., & Scanziani, M. (2015) · Nature Neuroscience · doi:10.1038/nn.4153 · Cited by 174 · see citing works
Mouse Motor Cortex Coordinates the Behavioral Response to Unpredicted Sensory Feedback
Heindorf, M., Arber, S., & Keller, G. B. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.046 · Cited by 141 · see citing works
Self-citationVélez‐Fort, M., Bracey, E. F., Keshavarzi, S., Rousseau, C. V., Cossell, L., Lenzi, S. C., Strom, M., & Margrie, T. W. (2018) · Neuron · doi:10.1016/j.neuron.2018.02.023 · Cited by 125 · see citing works
Noyas, W. (1887) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-188706000-00006 · Cited by 115 · see citing works
Yang, W., Carrasquillo, Y., Hooks, B. M., Nerbonne, J. M., & Burkhalter, A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2515-13.2013 · Cited by 94 · see citing works
Late onset autism and anti-NMDA-receptor encephalitis
Creten, C., van der Zwaan, S., Blankespoor, R. J., Maatkamp, A., Nicolai, J., van Os, J., & Schieveld, J. N. M. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60548-5 · Cited by 77 · see citing works
A Brief History of the “Feature Detector”
Martin, K. A. C. (1994) · Cerebral Cortex · doi:10.1093/cercor/4.1.1 · Cited by 73 · see citing works
Visual cortex: Fatigue and adaptation
Carandini, M. (2000) · Current Biology · doi:10.1016/s0960-9822(00)00637-0 · Cited by 73 · see citing works
Stanley, J., & Miall, R. C. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.043 · Cited by 66 · see citing works
Error Signals in Motor Cortices Drive Adaptation in Reaching
Inoue, M., Uchimura, M., & Kitazawa, S. (2016) · Neuron · doi:10.1016/j.neuron.2016.04.029 · Cited by 59 · see citing works
A robust role for motor cortex
Lopes, G., Nogueira, J., Dimitriadis, G., Menendez, J. A., Paton, J. J., & Kampff, A. R. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.971980 · Cited by 54 · see citing works
Stimulus relevance modulates contrast adaptation in visual cortex
Keller, A. J., Houlton, R., Kampa, B. M., Lesica, N. A., Mrsic‐Flogel, T. D., Keller, G. B., & Helmchen, F. (2017) · eLife · doi:10.7554/elife.21589 · Cited by 50 · see citing works
Self-citationKeller, A. J., & Martin, K. A. C. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0906-15.2015 · Cited by 23 · see citing works
A robust role for motor cortexPreprint
Lopes, G., Nogueira, J., Dimitriadis, G., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/058917 · Cited by 20 · see citing works
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Conscious Processing and the Global Neuronal Workspace Hypothesis
Mashour, G. A., Roelfsema, P., Changeux, J.-P., & Dehaene, S. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.026 · Cited by 1,164 · see citing works
“10 (Keller and Mrsic-Flogel, 2018; Kersten et al., 2004; Rao and Ballard, 1999), leading to a more precise functional interpretation of top-down broadcasting.”
BackgroundSingle-trial neural dynamics are dominated by richly varied movements
Musall, S., Kaufman, M. T., Juavinett, A., Gluf, S., & Churchland, A. K. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0502-4 · Cited by 1,057 · see citing works
Survey of spiking in the mouse visual system reveals functional hierarchy
Siegle, J. H., Jia, X., Durand, S., Gale, S. D., Bennett, C., Graddis, N., Heller, G. R., Ramirez, T. K., Choi, H., Luviano, J., et al. (2021) · Nature · doi:10.1038/s41586-020-03171-x · Cited by 610 · see citing works
The neural architecture of language: Integrative modeling converges on predictive processing
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517 · see citing works
A hierarchy of linguistic predictions during natural language comprehension
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201968119 · Cited by 343 · see citing works
Evaluating the neurophysiological evidence for predictive processing as a model of perception
Walsh, K. S., McGovern, D. P., Clark, A., & O'Connell, R. G. (2020) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14321 · Cited by 333 · see citing works
Large-scale neural recordings call for new insights to link brain and behavior
Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00980-9 · Cited by 324 · see citing works
Brains and algorithms partially converge in natural language processing
Caucheteux, C., & King, J.-R. (2022) · Communications Biology · doi:10.1038/s42003-022-03036-1 · Cited by 318 · see citing works
Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
Rathi, N., Chakraborty, I., Kosta, A. K., Sengupta, A., Ankit, A., Panda, P., & Roy, K. (2022) · ACM Computing Surveys · doi:10.1145/3571155 · Cited by 274 · see citing works
Causes and consequences of representational drift
Rule, M. E., O’Leary, T., & Harvey, C. D. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.08.005 · Cited by 227 · see citing works
Prediction and memory: A predictive coding account
Barron, H. C., Auksztulewicz, R., & Friston, K. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101821 · Cited by 220 · see citing works
The power of predictions: An emerging paradigm for psychological research
Hutchinson, J. B., & Barrett, L. F. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419831992 · Cited by 216 · see citing works
fMRI reveals language-specific predictive coding during naturalistic sentence comprehension
Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215 · see citing works
The mouse prefrontal cortex: Unity in diversity
Le Merre, P., Ährlund-Richter, S., & Carlén, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.035 · Cited by 207 · see citing works
Feedback generates a second receptive field in neurons of the visual cortex
Keller, A. J., Roth, M., & Scanziani, M. (2020) · Nature · doi:10.1038/s41586-020-2319-4 · Cited by 206 · see citing works
Jordan, R., & Keller, G. B. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.024 · Cited by 207 · see citing works
Self-citationA framework for understanding the pathophysiology of functional neurological disorder
Drane, D. L., Fani, N., Hallett, M., Khalsa, S. S., Perez, D. L., & Roberts, N. A. (2020) · CNS Spectrums · doi:10.1017/s1092852920001789 · Cited by 205 · see citing works
Prediction signals in the cerebellum: Beyond supervised motor learning
Hull, C. (2020) · eLife · doi:10.7554/elife.54073 · Cited by 196 · see citing works
A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex
Keller, A. J., Dipoppa, M., Roth, M., Caudill, M. S., Ingrosso, A., Miller, K. D., & Scanziani, M. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.013 · Cited by 177 · see citing works
Prediction during language comprehension: what is next?
Ryskin, R. A., & Nieuwland, M. S. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.08.003 · Cited by 183 · see citing works
Layer-specific integration of locomotion and sensory information in mouse barrel cortex
Ayaz, A., Stäuble, A., Hamada, M., Wulf, M.-A., Saleem, A. B., & Helmchen, F. (2019) · Nature Communications · doi:10.1038/s41467-019-10564-8 · Cited by 150 · see citing works
Movement and Performance Explain Widespread Cortical Activity in a Visual Detection Task
Salkoff, D. B., Zagha, E., McCarthy, E., & McCormick, D. A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz206 · Cited by 153 · see citing works
ERP and MEG correlates of visual consciousness: The second decade
Förster, J., Koivisto, M., & Revonsuo, A. (2020) · Consciousness and Cognition · doi:10.1016/j.concog.2020.102917 · Cited by 167 · see citing works
Forms of prediction in the nervous system
Teufel, C., & Fletcher, P. C. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0275-5 · Cited by 155 · see citing works
Movement-Related Signals in Sensory Areas: Roles in Natural Behavior
Parker, P. R. L., Brown, M. A., Smear, M. C., & Niell, C. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.05.005 · Cited by 149 · see citing works
Semedo, J. D., Jasper, A. I., Zandvakili, A., Krishna, A., Aschner, A., Machens, C. K., Kohn, A., & Yu, B. M. (2022) · Nature Communications · doi:10.1038/s41467-022-28552-w · Cited by 152 · see citing works
Neocortical Layer 1: an elegant solution to top-down and bottom-up integration
Schuman, B., Dellal, S., Prönneke, A., Machold, R., & Rudy, B. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100520-012117 · Cited by 148 · see citing works
Nonmonotonic Plasticity: How Memory Retrieval Drives Learning
Ritvo, V. J. H., Turk‐Browne, N. B., & Norman, K. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.06.007 · Cited by 146 · see citing works
Friston, K. J. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000426 · Cited by 141 · see citing works
Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex
Aitken, F., Menelaou, G., Warrington, O., Koolschijn, R. S., Corbin, N., Callaghan, M. F., & Kok, P. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3001023 · Cited by 134 · see citing works
Vezoli, J., Magrou, L., Goebel, R., Wang, X., Knoblauch, K., Vinck, M. A., & Kennedy, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117479 · Cited by 125 · see citing works
A cortical circuit for audio-visual predictions
Garner, A. R., & Keller, G. B. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00974-7 · Cited by 127 · see citing works
Self-citationDecoding Natural Sounds in Early “Visual” Cortex of Congenitally Blind Individuals
Vetter, P., Bola, Ł., Reich, L., Bennett, M., Muckli, L. F., & Amedi, A. (2020) · Current Biology · doi:10.1016/j.cub.2020.05.071 · Cited by 126 · see citing works
Prediction errors disrupt hippocampal representations and update episodic memories
Sinclair, A. H., Manalili, G. M., Brunec, I. K., Adcock, R. A., & Barense, M. D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2117625118 · Cited by 128 · see citing works
Chen, Y., Chen, X., Başerdem, B., Zhan, H., Li, Y., Davis, M. B., Kebschull, J. M., Zador, A. M., Koulakov, A. A., & Albeanu, D. F. (2022) · Cell · doi:10.1016/j.cell.2022.09.038 · Cited by 119 · see citing works
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0671-1 · Cited by 126 · see citing works
Where is the error? Hierarchical predictive coding through dendritic error computation
Mikulasch, F. A., Rudelt, L., Wibral, M., & Priesemann, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.09.007 · Cited by 128 · see citing works
An integrative, multiscale view on neural theories of consciousness
Storm, J. F., Klink, P. C., Aru, J., Senn, W., Goebel, R., Pigorini, A., Avanzini, P., Vanduffel, W., Roelfsema, P. R., Massimini, M., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.02.004 · Cited by 114 · see citing works
How development sculpts hippocampal circuits and function
Cossart, R., & Khazipov, R. (2021) · Physiological Reviews · doi:10.1152/physrev.00044.2020 · Cited by 119 · see citing works
Classic Psychedelic Drugs: Update on Biological Mechanisms
Vollenweider, F. X., & Smallridge, J. W. (2022) · Pharmacopsychiatry · doi:10.1055/a-1721-2914 · Cited by 112 · see citing works
Cooperative thalamocortical circuit mechanism for sensory prediction errors
Furutachi, S., Franklin, A. D., Aldea, A. M., Mrsic‐Flogel, T. D., & Hofer, S. B. (2024) · Nature · doi:10.1038/s41586-024-07851-w · Cited by 106 · see citing works
Self-citationA thalamocortical top-down circuit for associative memory
Pardi, M. B., Vogenstahl, J., Dalmay, T., Spanò, T., Pu, D.-L., Naumann, L., Kretschmer, F., Sprekeler, H., & Letzkus, J. J. (2020) · Science · doi:10.1126/science.abc2399 · Cited by 103 · see citing works
The influence of subcortical shortcuts on disordered sensory and cognitive processing.
McFadyen, J., Dolan, R. J., & Garrido, M. I. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0287-1 · Cited by 91 · see citing works
Predictive Coding: a Theoretical and Experimental Review
Millidge, B., Seth, A., & Buckley, C. L. (2021) · arXiv · doi:10.48550/arxiv.2107.12979 · Cited by 106 · see citing works
Coding Principles in Adaptation
Weber, A. I., Krishnamurthy, K., & Fairhall, A. L. (2019) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091718-014818 · Cited by 103 · see citing works
Experience shapes activity dynamics and stimulus coding of VIP inhibitory cells
Garrett, M., Manavi, S., Roll, K., Ollerenshaw, D. R., Groblewski, P. A., Ponvert, N. D., Kiggins, J. T., Casal, L., Mace, K. D., Williford, A., et al. (2020) · eLife · doi:10.7554/elife.50340 · Cited by 102 · see citing works
A virtual reality system to analyze neural activity and behavior in adult zebrafish
Huang, K., Rupprecht, P., Frank, T., Kawakami, K., Bouwmeester, T., & Friedrich, R. W. (2020) · Nature Methods · doi:10.1038/s41592-020-0759-2 · Cited by 96 · see citing works
Regulation of circuit organization and function through inhibitory synaptic plasticity
Wu, Y. K., Miehl, C., & Gjorgjieva, J. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.10.006 · Cited by 102 · see citing works
Livneh, Y., & Andermann, M. L. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.036 · Cited by 106 · see citing works
Language prediction mechanisms in human auditory cortex
Forseth, K. J., Hickok, G. S., Rollo, P. S., & Tandon, N. (2020) · Nature Communications · doi:10.1038/s41467-020-19010-6 · Cited by 97 · see citing works
Representation Wars: Enacting an Armistice Through Active Inference
Constant, A., Clark, A., & Friston, K. J. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.598733 · Cited by 97 · see citing works
Jordan, R., & Keller, G. B. (2023) · eLife · doi:10.7554/elife.85111 · Cited by 126 · see citing works
Self-citationParcels and particles: Markov blankets in the brain
Friston, K. J., Fagerholm, E. D., Zarghami, T. S., Parr, T., Hipólito, I., Magrou, L., & Razi, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00175 · Cited by 90 · see citing works
Gastrointestinal Interoception in Eating Disorders: Charting a New Path
Khalsa, S. S., Berner, L. A., & Anderson, L. M. (2022) · Current Psychiatry Reports · doi:10.1007/s11920-022-01318-3 · Cited by 100 · see citing works
Kauvar, I., Machado, T. A., Yuen, E., Kochalka, J., Choi, M., Allen, W. E., Wetzstein, G., & Deisseroth, K. A. (2020) · Neuron · doi:10.1016/j.neuron.2020.04.023 · Cited by 89 · see citing works
Cortico-cortical feedback engages active dendrites in visual cortex
Fişek, M., Herrmann, D., Egea-Weiss, A., Cloves, M., Bauer, L., Lee, T.-Y., Russell, L. E., & Häusser, M. (2023) · Nature · doi:10.1038/s41586-023-06007-6 · Cited by 80 · see citing works
Allostasis as a core feature of hierarchical gradients in the human brain
Katsumi, Y., Theriault, J. E., Quigley, K. S., & Barrett, L. F. (2022) · Network Neuroscience · doi:10.1162/netn_a_00240 · Cited by 88 · see citing works
Inferring neural activity before plasticity as a foundation for learning beyond backpropagation
Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01514-1 · Cited by 85 · see citing works
Cell-Type-Specific Outcome Representation in the Primary Motor Cortex
Levy, S., Lavzin, M., Benisty, H., Ghanayim, A., Dubin, U., Achvat, S., Brosh, Z., Aeed, F., Mensh, B. D., Schiller, Y., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.006 · Cited by 88 · see citing works
Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex
Casado-Román, L., Carbajal, G. V., Pérez‐González, D., & Malmierca, M. S. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3001019 · Cited by 82 · see citing works
Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition
Wang, X.-J. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-110920-035434 · Cited by 83 · see citing works
Prior Expectations of Motion Direction Modulate Early Sensory Processing
Aitken, F., Turner, G., & Kok, P. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0537-20.2020 · Cited by 76 · see citing works
How deep is the brain? The shallow brain hypothesis
Suzuki, M., Pennartz, C. M. A., & Aru, J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00756-z · Cited by 79 · see citing works
O'Toole, S. M., Oyibo, H. K., & Keller, G. B. (2023) · Neuron · doi:10.1016/j.neuron.2023.08.015 · Cited by 101 · see citing works
Self-citationSequential and efficient neural-population coding of complex task information
Koay, S. A., Charles, A. S., Thiberge, S. Y., Brody, C. D., & Tank, D. W. (2021) · Neuron · doi:10.1016/j.neuron.2021.10.020 · Cited by 75 · see citing works
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Kuperberg, G. R. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12518 · Cited by 79 · see citing works
Ryskin, R. A., Lévy, R., & Fedorenko, E. G. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107258 · Cited by 74 · see citing works
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Cisek, P., & Green, A. M. (2024) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2024.102859 · Cited by 84 · see citing works
Ficco, L., Mancuso, L., Manuello, J., Teneggi, A., Liloia, D., Duca, S., Costa, T., Kovács, G., & Cauda, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-95603-5 · Cited by 77 · see citing works
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Guitchounts, G., Masis, J. A., Wolff, S. B. E., & Cox, D. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.014 · Cited by 72 · see citing works
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Widmer, F. C., O'Toole, S. M., & Keller, G. B. (2022) · eLife · doi:10.7554/elife.71476 · Cited by 74 · see citing works
Self-citationNot so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas
Avitan, L., & Stringer, C. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.019 · Cited by 69 · see citing works
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McGovern, H. T., Grimmer, H. J., Doss, M. K., Hutchinson, B. T., Timmermann, C., Lyon, A., Corlett, P. R., & Laukkonen, R. (2024) · Communications Psychology · doi:10.1038/s44271-024-00120-6 · Cited by 74 · see citing works
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Sammons, M., Popescu, M. C., Chi, J., Liberles, S. D., Gogolla, N., & Rolls, A. (2024) · Cell · doi:10.1016/j.cell.2024.08.050 · Cited by 72 · see citing works
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Hoel, E. (2021) · Patterns · doi:10.1016/j.patter.2021.100244 · Cited by 70 · see citing works
Antunes, F. M., & Malmierca, M. S. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.721186 · Cited by 71 · see citing works
Learning prediction error neurons in a canonical interneuron circuit
Hertäg, L., & Sprekeler, H. (2020) · eLife · doi:10.7554/elife.57541 · Cited by 71 · see citing works
Predictive processing of scenes and objects
Peelen, M. V., Berlot, E., & de Lange, F. P. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00254-0 · Cited by 79 · see citing works
Precise movement-based predictions in the mouse auditory cortex
Audette, N. J., Zhou, W., Chioma, A. L., & Schneider, D. M. (2022) · Current Biology · doi:10.1016/j.cub.2022.09.064 · Cited by 70 · see citing works
The locus coeruleus as a global model failure system
Jordan, R. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.11.006 · Cited by 77 · see citing works
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Mathias, B., & von Kriegstein, K. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.10.007 · Cited by 70 · see citing works
Taniguchi, T., Murata, S., Suzuki, M., Ognibene, D., Lanillos, P., Uğur, E., Jamone, L., Nakamura, T., Ciria, A., Lara, B., et al. (2023) · Advanced Robotics · doi:10.1080/01691864.2023.2225232 · Cited by 67 · see citing works
A sensory–motor theory of the neocortex
Rao, R. P. N. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01673-9 · Cited by 84 · see citing works
Halvagal, M. S., & Zenke, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01460-y · Cited by 66 · see citing works
Morgan, A., Petro, L. S., & Muckli, L. F. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0852-19.2019 · Cited by 66 · see citing works
Long-range functional loops in the mouse olfactory system and their roles in computing odor identity
Chae, H., Banerjee, A., Dussauze, M., & Albeanu, D. F. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.005 · Cited by 69 · see citing works
Interneuron Types and Their Circuits in the Basolateral Amygdala
Hájos, N. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.687257 · Cited by 61 · see citing works
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 64 · see citing works
Torigoe, M., Islam, T., Kakinuma, H., Fung, C. C. A., Isomura, T., Shimazaki, H., Aoki, T., Fukai, T., & Okamoto, H. (2021) · Nature Communications · doi:10.1038/s41467-021-26010-7 · Cited by 61 · see citing works
Brain-wide representations of ongoing behavior: a universal principle?
Kaplan, H. S., & Zimmer, M. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.02.008 · Cited by 57 · see citing works
Bakhtiari, S., Mineault, P., Lillicrap, T., Pack, C. C., & Richards, B. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.18.448989 · Cited by 60 · see citing works
Behaviorally relevant decision coding in primary somatosensory cortex neurons
Buetfering, C., Zhang, Z., Pitsiani, M., Smallridge, J., Boven, E., McElligott, S., & Häusser, M. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01151-0 · Cited by 59 · see citing works
Predictive coding of natural images by V1 firing rates and rhythmic synchronization
Uran, C., Peter, A., Lazăr, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., Roese, R., Fries, P., Singer, W., & Vinck, M. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.01.002 · Cited by 64 · see citing works
Rule, M. E., & O’Leary, T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106692119 · Cited by 67 · see citing works
Laminar-specific cortico-cortical loops in mouse visual cortex
Young, H., Belbut, B., Baeta, M., & Petreanu, L. (2021) · eLife · doi:10.7554/elife.59551 · Cited by 67 · see citing works
The neural coding framework for learning generative models
Ororbia, A. G., & Kifer, D. (2022) · Nature Communications · doi:10.1038/s41467-022-29632-7 · Cited by 58 · see citing works
Sensorimotor strategies and neuronal representations for shape discrimination
Rodgers, C. C., Nogueira, R., Pil, B. C., Greeman, E. A., Park, J. M., Hong, Y. K., Fusi, S., & Bruno, R. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.019 · Cited by 57 · see citing works
- Authors
- Keller, G. B., & Mrsic-Flogel, T. D.
- Affiliations
- University of Basel (CH)
- Corresponding author
- Keller, Georg B.
- ORCID
- Keller, Georg B.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 100
- Issue
- 2
- Pages
- 424-435
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC6400266
- OpenAlex
- W2898208057
- Cited by
- 836
- References
- 133
- Citations by year
- 2026: 120 · 2025: 128 · 2024: 139 · 2023: 152 · 2022: 95 · 2021: 85 · 2020: 78 · 2019: 38 · 2018: 1
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 388 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Gatsby Charitable Foundation (GAT3212, GAT3361)
- Wellcome (090843/E/09/Z)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- predictive processing, cortical circuits, sensory processing, cortical function, falsifiability, computational neuroscience, neural circuits, brain disorders
- MeSH terms
- Animals, Computer Simulation, Humans, Models, Neurological, Sensation, Neocortex
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.
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