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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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“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
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“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]
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“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]
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“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]
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“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]
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“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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Metadata
- 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.
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 ›
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