How deep is the brain? The shallow brain hypothesis
Open accessAbstract
Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.
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In the typical mammalian brain, recurrent loops between the subcortex and regions of the cortex are a vital and almost universal feature of processing.
“In the typical mammalian brain, however, recurrent loops between the subcortex and regions of the cortex are a vital and almost universal feature of processing [85].”
From Phenomenal interface theory: a model for basal consciousness (2025) · cites this paper as [85]
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Prefrontal Dopamine in Flexible Adaptation to Environmental Changes: A Game for Two Players
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Neuromorphic engineering: Artificial brains for artificial intelligence
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Network structure governs Drosophila brain functionality
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From hippocampal neurons to broad spiking neural networks
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Thalamo-Cortical Interaction for Incremental Binding in Mental Contour-TracingPreprint
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A comparison between humans and AI at recognizing objects in unusual posesPreprint
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Shallow Brain Residual Network for Classifying UAVs and Birds in ISAC Base Stations
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Multimodal mismatch responses in mouse auditory cortexPreprint
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Multimodal mismatch responses in mouse auditory cortexPreprint
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Dynamic Reservoir Computing with Physical Neuromorphic Networks
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Control of representation updating by higher-order thalamus enables history-based decision-making
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The prelimbic cortex arbitrates memory-guided conflict resolution
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Loops all the way up: Recurrency as an implementation-first primitive for consciousness
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Switching Patterns of Cortical–Subcortical Interaction in the Human Brain
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Prefrontal orchestration: a cortical network for rodent motor inhibitionPreprint
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Günümüz(de) Liderlik Araştırmaları
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Self-citationDorsomedial striatal neurons encode visual features
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- Authors
- Suzuki, M., Pennartz, C. M. A., & Aru, J.
- Affiliations
- University of Amsterdam (NL)
- University of Tartu (EE)
- Corresponding author
- Aru, Jaan
- Venue
- Nature Reviews Neuroscience
- Published
- 2023-10-27
- Volume
- 24
- Issue
- 12
- Pages
- 778-791
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W4387996733
- Cited by
- 79
- References
- 224
- Citations by year
- 2026: 21 · 2025: 26 · 2024: 28 · 2023: 4
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- green
- handle.uba.uva.nl
- Licence
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion, other-oa)
- Funders
- Brain Science Foundation
- European Commission (HORIZON2020, 945539)
- Eesti Teadusagentuur (PSG728)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- thalamocortical loop, cortical microcircuits, predictive coding, deep learning architectures, neural network architecture, massively parallel computing
- MeSH terms
- Animals, Brain, Humans, Mammals, Neural Networks, Computer
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