Deep gated Hebbian predictive coding accounts for emergence of complex neural response properties along the visual cortical hierarchy
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Abstract
Abstract Predictive coding provides a computational paradigm for modelling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we develop a scalable, deep predictive coding network that is trained using a Hebbian learning rule. Without a priori constraints that would force model neurons to respond like biological neurons, the model exhibits properties similar to those reported in experimental studies. We analyze low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increases systematically across ascending areas in the model hierarchy. A further emergent network property is that representations for different object classes become more distinguishable from lower to higher areas. Thus, deep predictive coding networks can be effectively trained using biologically plausible principles and exhibit emergent properties that have been experimentally identified along the visual cortical hierarchy. Significance Statement Understanding brain mechanisms of perception requires a computational approach based on neurobiological principles. Many deep learning architectures are trained by supervised learning from large sets of labeled data, whereas biological brains must learn from unlabeled sensory inputs. We developed a Predictive Coding methodology for building scalable networks that mimic deep sensory cortical hierarchies, perform inference on the causes of sensory inputs and are trained by unsupervised, Hebbian learning. The network models are well-behaved in that they faithfully reproduce visual images based on high-level, latent representations. When ascending the sensory hierarchy, we find increasing image selectivity, sparseness and generalizability for object classification. These models show how a complex neuronal phenomenology emerges from biologically plausible, deep networks for unsupervised perceptual representation.
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In RPT, stage 4 attended stimuli lead to deep, global recurrent processing reaching higher levels including prefrontal and executive areas, providing contents accessible to executive systems (AC in addition to PC), for use in working memory, reports, and other behavioral responses, i.e., ignition in GNWT.
“Stage 4 Given sufficient time, attended stimuli lead to deep, global recurrent processing (Figure 3D), 27,102,105 reaching higher levels, including prefrontal, executive areas, providing contents accessible to the brain's executive systems (AC in addition to PC), for use in, e.g., working memory, reports, and other behavioral responses, i.e., ''ignition'' in GNWT (see above).”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [105]
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 measuredNREP has provided computational network models based on sensory cortical hierarchies, which illustrate several integrative processes operating at the level of specific image representation, view-invariant object representation, figure-ground segmentation during ego- and object-motion, and predictive cross-modal interactions.
“NREP has provided computational network models based on sensory cortical hierarchies, which illustrate several integrative processes operating at the level of specific image representation, view-invariant object representation, figure-ground segmentation during ego- and object-motion, and predictive cross-modal interactions (Brucklacher et al., 2023; Brucklacher, Pezzulo, et al., 2025; Dora et al.”
From Integrated information and predictive processing theories of consciousness: An adversarial collaborative review (2026) · cites this paper as Dora et al., 2021
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredFollowing this strategy, researchers have demonstrated, for instance, the neurobiological plausibility of predictive processing-based representational models in spiking neural networks, and reproduced the increase in population sparsity and single-neuron image selectivity observed when ascending the visual cortical hierarchy.
“Following this strategy, researchers have demonstrated, for instance, the neurobiological plausibility of predictive processing-based representational models in spiking neural networks (Lee et al., 2024), and reproduced the increase in population sparsity and singleneuron image selectivity observed when ascending the visual cortical hierarchy (Dora et al., 2021).”
From Integrated information and predictive processing theories of consciousness: An adversarial collaborative review (2026) · cites this paper as Dora et al., 2021
Numerous experimental results support RPT, e.g., from masking, change blindness, inattention, attentional blink, neglect, and extinction, but do not exclude other theories.
“Numerous experimental results support RPT, e.g., from masking, change blindness, inattention, attentional blink, neglect, and extinction, 8,101,102,105 but do not exclude other theories.”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [105]
The Introduction discusses experimental evidence for predictive coding, not evidence supporting recurrent processing theory (RPT). Searches for masking, change blindness, inattention, attentional blink, neglect, extinction, and comparisons with other theories found no passage addressing the attributed claim; the References have no page number in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe late positivity (LP) ERP wave (P300/P3b) shows characteristics of stage 4 and may thus reflect AC, in agreement with GNWT.
“The late (300 ms) positivity (LP) ERP wave (P300/ P3b 29 ) shows characteristics of stage 4 and may thus reflect AC (Figure 3D), 102,105 in agreement with GNWT.28”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [105]
The Results and Materials and Methods describe areas of a visual predictive-coding model, not stages of conscious access. Searches for late positivity, P300/P3b, ERP, and global neuronal workspace theory found no passage in our copy linking the ERP wave to stage 4 or AC. (LLM, conf=0.76)
Experimental — accuracy not yet measured
In RPT, stage 1 local feedforward processing, if not boosted by attention, stays local, failing to proceed to stage 2, yielding only subliminal processing without PC or AC.
“Stage 1 Local (''superficial'') feedforward processing: a fast feedforward sweep (FFS, 100-200ms) of activity carries information from primary to higher visual cortices, where image features are extracted.However, if not boosted by attention, processing stays local, failing to proceed to stage 2, yielding only ''subliminal'' processing 106 without PC or AC 27,102,105 (Figure 3A).”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [105]
The Introduction and Model Architecture section describe feedforward information flow through a visual hierarchy, but do not address RPT stages or whether attention determines progression to a second stage. Searches for attention, subliminal processing, and phenomenal or access consciousness found no passage supporting the attributed outcome. (LLM, conf=0.72)
Experimental — accuracy not yet measuredIn RPT, global recurrent processing engages robust, low-capacity working memory, whereas local recurrent processing engages large-capacity, very brief, sensory (iconic), and fragile memory in sensory cortices, which is easily overwritten.
“Whereas global RP engages robust, low-capacity working memory (WM), local RP engages large-capacity, very brief, sensory (iconic), and fragile memory (SM, FM; 0.1-few seconds) in sensory cortices, which is easily overwritten by other stimuli.105”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [105]
The Materials and Methods section (Model Architecture With Receptive Fields; page unavailable) describes recurrent connections between visual-cortical areas, not a distinction between global and local recurrent processing or their memory functions. Searches for working memory, iconic and fragile memory, and stimulus overwriting found no passage in our copy addressing the claimed capacities or durations. (LLM, conf=0.76)
Experimental — accuracy not yet measuredRPT is more compatible with GNWT than their different definitions would suggest, because both attribute a key role to the prefrontal-parietal network regulating attentional signals.
“In this sense, RPT is more compatible with GNWT than their different definitions would suggest: they both attribute a key role to the prefrontal-parietal network regulating attentional signals.102,105”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [105]
The Discussion’s “Comparison With Previous Models” section mentions a predictive-coding model that reproduces aspects of attention, but does not compare RPT with GNWT. Searches for both theories and for a prefrontal-parietal network regulating attention found no passage addressing the attributed claim in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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Amit, Y. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00018 · Cited by 38 · see citing works
Glutamate Receptor Modulation Is Restricted to Synaptic Microdomains
Lür, G., & Higley, M. J. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.06.029 · Cited by 37 · see citing works
Statistics of visual responses in primate inferotemporal cortex to object stimuli
Lehky, S. R., Kiani, R., Esteky, H., & Tanaka, K. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00990.2010 · Cited by 32 · see citing works
Suzuki, W., Matsumoto, K., & Tanaka, K. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.1532-06.2006 · Cited by 24 · see citing works
Khawaja, F. A., Alonso, A. A., & Bourque, C. W. (2007) · Hippocampus · doi:10.1002/hipo.20365 · Cited by 18 · see citing works
A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs
Dora, S., Pennartz, C. M. A., & Bohté, S. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01424-7_45 · Cited by 16 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Pennartz C. M. A. (2015). The Brains Representational Power. Cambridge, MA: The MIT press.
Cited by
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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
Self-citationHow deep is the brain? The shallow brain hypothesis
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Self-citationAmunts, K., Axer, M., Banerjee, S., Bitsch, L., Bjaalie, J. G., Brauner, P., Brovelli, A., Calarco, N., Carrère, M., Caspers, S., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00137 · Cited by 53 · see citing works
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Self-citationBellet, M. E., Gay, M., Bellet, J., Jarraya, B., Dehaene, S., van Kerkoerle, T., & Panagiotaropoulos, T. I. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113952 · Cited by 30 · see citing works
Evers, K., Farisco, M., & Pennartz, C. M. A. (2024) · Consciousness and Cognition · doi:10.1016/j.concog.2024.103668 · Cited by 22 · see citing works
Self-citationBrucklacher, M., Bohté, S. M., Mejias, J. F., & Pennartz, C. M. A. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1207361 · Cited by 15 · see citing works
“Unlike (Dora et al., 2021), the present implementation also does not require weight regularization that depends on information not readily available at the synapses.”
BackgroundInfluentialSelf-citationPredictive coding with spiking neurons and feedforward gist signaling
Lee, K., Dora, S., Mejias, J. F., Bohte, S. M., & Pennartz, C. M. A. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1338280 · Cited by 14 · see citing works
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Self-citationZhang, M., Chitic, R., & Bohté, S. M. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013112 · Cited by 4 · see citing works
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Corcoran, A. W., Haun, A. M., Dorman, R., Tononi, G., Friston, K. J., & Pennartz, C. M. A. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106742 · Cited by 2 · see citing works
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An integrative, multiscale view on consciousness theories_Eary version of paper in press in NEURONPreprint
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) · doi:10.31234/osf.io/9byzu · Cited by 1 · see citing works
Self-citationPredictive processing in neuroscience, computational modeling and psychology
Brucklacher, M., Lee, K., Moreni, G., Mejías, J. F., Bohté, S. M., & Pennartz, C. M. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00201-1 · Cited by 1 · see citing works
Self-citationCan working memory be explained by predictive coding?Preprint
Feng, M. (2023) · doi:10.31237/osf.io/sqfr9
Can working memory be explained by predictive coding?Preprint
Feng, M., & Nunez, M. D. (2025) · doi:10.31234/osf.io/5zbc6_v1
Can working memory be explained by predictive coding?Preprint
Feng, M., & Nunez, M. D. (2025) · doi:10.31234/osf.io/5zbc6_v2
van Daatselaar, J., Sikkens, T., Müller, M., Pennartz, C. M. A., Olcese, U., & Bosman, C. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.29.662227
Self-citationDeep learning and neuroscience : a match made in heaven?
Choksi, B. (2022) · theses.fr (ABES) · doi:10.70675/42802d90z0c77z4de8z829dz508145498217
Study of Sequence Encoding in the Prefrontal Cortex of Non-Human Primates
Schmidt, M. D. (2024) · theses.fr (ABES) · doi:10.70675/6c17db3bz5de8z4550z8deezf289d8bb4468
Pennartz, C. M. A. (2026) · Physics of Life Reviews · doi:10.1016/j.plrev.2026.09.004
Self-citation
- Authors
- Dora, S., Bohte, S. M., & Pennartz, C. M. A.
- Affiliations
- University of Ulster (GB)
- University of Amsterdam (NL)
- Corresponding author
- Dora, Shirin
- Venue
- Frontiers in Computational Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2021-07-28
- Volume
- 15
- Pages
- 666131-666131
- ISSN
- 1662-5188 (ISSN-L)
- PMC
- PMC8355371
- OpenAlex
- W3184778028
- Cited by
- 24
- References
- 90
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 4 · 2022: 2
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Computational Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Ulster University Research Portal (Ulster University) (submittedVersion, cc-by)
- Funders
- Horizon 2020 Framework Programme (785907, 945539)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- predictive coding, deep neural network, visual cortex, cortical hierarchy, orientation selectivity, object selectivity, feedback connectivity, latent representations, image reconstruction, regularization
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