ReviewNature reviews. Neuroscience2023
How deep is the brain? The shallow brain hypothesis.
Review in Nature reviews. Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed, 67 citations in OpenAlex.
- A computational architecture incorporating shallow brain networks: integrating parallel cortical and subcortical processing.Current research in neurobiology · 2026Article
- Network structure governs Drosophila brain functionality.Fundamental research · 2026Article
- 'Backpropagation and the brain' realized in cortical error neuron microcircuits.PLoS computational biology · 2026Article
- Soft sonocapacitor with topologically integrated piezodielectric nanospheres enables wireless epidural closed-loop neuromodulation.Nature communications · 2026Article
- Motor and operative intentionality in computational phenomenology: from shallow to deep active inference models.Neuroscience of consciousness · 2026Article
- Phenomenal interface theory: a model for basal consciousness.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Switching Patterns of Cortical-Subcortical Interaction in the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Connectome-constrained ligand-receptor interaction analysis for understanding brain network communication.Nature communications · 2025Article
- Article
- A model of thalamo-cortical interaction for incremental binding in mental contour-tracing.PLoS computational biology · 2025Article
- Beyond Mechanism-Extending Our Concepts of Causation in Neuroscience.The European journal of neuroscience · 2025Review
- Article
- Neuromorphic engineering: Artificial brains for artificial intelligence.Annals of the New York Academy of Sciences · 2024Article
- Confidence and second-order errors in cortical circuits.PNAS nexus · 2024Article
- Neuroethics and AI ethics: a proposal for collaboration.BMC neuroscience · 2024Review
- Consciousness and sleep.Neuron · 2024Review
- Imagination vs. routines: festive time, weekly time, and the predictive brain.Frontiers in human neuroscience · 2024Article
- Predictive coding with spiking neurons and feedforward gist signaling.Frontiers in computational neuroscience · 2024Article
- Prefrontal Dopamine in Flexible Adaptation to Environmental Changes: A Game for Two Players.Biomedicines · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.