Cited by 79Neural dynamics and brain functionMemory and Neural MechanismsNeural Networks and Applications

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How deep is the brain? The shallow brain hypothesis

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Suzuki, Mototaka, Pennartz, Cyriel M. A., Aru, Jaan·2023·Nature Reviews Neuroscience

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Abstract

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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“The next aggregate levels are local (within-area) recurrent processing in cortical microcircuits (C, upper left; from Suzuki et al. 138 ) and cortico-thalamic loops (C, HO: higher order).”
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See all 79

Authors: Suzuki, M., Pennartz, C. M. A., & Aru, J.
Affiliations: University of Amsterdam (NL)
University of Tartu (EE)
Corresponding author: Aru, Jaan
ORCID: Suzuki, Mototaka
Pennartz, Cyriel M. A.
Aru, Jaan
Venue: Nature Reviews Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2023-10-27
Volume: 24
Issue: 12
Pages: 778-791
ISSN: 1471-003X (ISSN-L)
1471-0048
DOI: 10.1038/s41583-023-00756-z
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: other-oa
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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