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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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Comparative genomics of autism and schizophrenia
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Control of synaptic plasticity in deep cortical networks
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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
Self-citationNeural 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
Self-citationGating and control of primary visual cortex by pulvinar
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Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex
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Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion
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Learned spatiotemporal sequence recognition and prediction in primary visual cortex
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Shared Molecular Neuropathology Across Major Psychiatric Disorders Parallels Polygenic Overlap
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Predictive coding as a model of response properties in cortical area V1
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Late-onset anti–NMDA receptor encephalitis
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Activity recall in a visual cortical ensemble
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Distinct recurrent versus afferent dynamics in cortical visual processing
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Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex
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Mismatch Receptive Fields in Mouse Visual Cortex
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Self-citationAttention and primary visual cortex
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Mouse Motor Cortex Coordinates the Behavioral Response to Unpredicted Sensory Feedback
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Late onset autism and anti-NMDA-receptor encephalitis
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A Brief History of the “Feature Detector”
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Visual cortex: Fatigue and adaptation
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A robust role for motor cortex
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Stimulus relevance modulates contrast adaptation in visual cortex
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Self-citationKeller, A. J., & Martin, K. A. C. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0906-15.2015 · Cited by 23
A robust role for motor cortexPreprint
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Research works printed but not linked: 1
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“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
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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
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