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The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost
Open accessAbstract
Neuroscientists have become used to a number of “facts” about the human brain: It has 100 billion neurons and 10- to 50-fold more glial cells; it is the largest-than-expected for its body among primates and mammals in general, and therefore the most cognitively able; it consumes an outstanding 20% of the total body energy budget despite representing only 2% of body mass because of an increased metabolic need of its neurons; and it is endowed with an overdeveloped cerebral cortex, the largest compared with brain size. These facts led to the widespread notion that the human brain is literally extraordinary: an outlier among mammalian brains, defying evolutionary rules that apply to other species, with a uniqueness seemingly necessary to justify the superior cognitive abilities of humans over mammals with even larger brains. These facts, with deep implications for neurophysiology and evolutionary biology, are not grounded on solid evidence or sound assumptions, however. Our recent development of a method that allows rapid and reliable quantification of the numbers of cells that compose the whole brain has provided a means to verify these facts. Here, I review this recent evidence and argue that, with 86 billion neurons and just as many nonneuronal cells, the human brain is a scaled-up primate brain in its cellular composition and metabolic cost, with a relatively enlarged cerebral cortex that does not have a relatively larger number of brain neurons yet is remarkable in its cognitive abilities and metabolism simply because of its extremely large number of neurons.
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“Our neural network parameters represent a computational analog to biological neural capacity, directly reflecting the studies showing strong correlations between brain volume and total neuron count across species, humans, and mice brains (68–70).”
From What if eye...? Computationally recreating vision evolution (2025) · cites this paper as [70]
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“During much of evolutionary history, when food scarcity and caloric deprivation was a major evolutionary pressure, mechanisms evolved to minimize muscle size and thus decrease non-essential energy expenditure42,44,55.”
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“minimizing muscle mass during caloric restriction can substantially reduce energy expenditure, thus sparing energy for the maintenance of other more critical organ systems42–44.”
From GDF8 and activin A blockade protects against GLP-1–induced muscle loss while enhancing fat loss in obese male mice and non-human primates (2025) · cites this paper as [42]
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“Beyond identifiable causes, a large part of phenotype variability across patients with the same ABCD1 genotype may “simply” be due to stochasticity, a factor hitherto considered “noise” by Cartesian researchers.”
From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [135]
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“Brain evolution may often be driven by adaptive changes in cellular composition or molecular expression within conserved structures4–6.”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [5]
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“The cortical reorganization hypothesis suggests that selection for absolutely larger brains-and concomitant cortical reorganization-was the predominant mechanism supporting cognitive evolution (77, 91, 100-106, 120).”
From The evolution of self-control. (2014) · cites this paper as [105]
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Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
(1982) · Trends in Neurosciences · doi:10.1016/0166-2236(82)90076-5 · Cited by 9,150
An Energy Budget for Signaling in the Grey Matter of the Brain
Attwell, D., & Laughlin, S. B. (2001) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-200110000-00001 · Cited by 3,543
Henry, M. A. (1995) · The Journal of the American Dental Association · doi:10.14219/jada.archive.1995.0108 · Cited by 3,454
Specification of Cerebral Cortical Areas
Rakic, P. (1988) · Science · doi:10.1126/science.3291116 · Cited by 3,127 · cited 1×
“Cortical expansion is commonly envisioned as occurring laterally, through the increase of the number of progenitor cells in the subventricular zone and the consequent addition of radial columns containing a constant number of neurons across species (38).”
BackgroundScaling: Why is Animal Size so Important?
Schmidt-Nielsen, K. (1984) · doi:10.1017/cbo9781139167826 · Cited by 2,677
Scaling Why is animal size so important?
Schmidt‐Nielsen, K. (1984) · Cited by 2,654
Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Filho, W. J., Lent, R., & Herculano‐Houzel, S. (2009) · Journal of Comparative Neurology · doi:10.1002/cne.21974 · Cited by 2,337
Self-citationThe expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution
Aiello, L. C., & Wheeler, P. (1995) · Current Anthropology · doi:10.1086/204350 · Cited by 2,291
Kleiber, M. (1932) · Hilgardia · doi:10.3733/hilg.v06n11p315 · Cited by 2,222
Research works printed but not linked: 1
- GC Conroy Primate Evolution (WW Norton, New York, 1990).
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Day, L. B., & Lindsay, W. R. (2016) · Brain Behavior and Evolution · doi:10.1159/000446341 · Cited by 4
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Young, J. (2022) · Time and Mind · doi:10.1080/1751696x.2022.2103727 · Cited by 2
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Metadata
- Authors
- Herculano-Houzel, S.
- Affiliations
- Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, 21941-902, Rio de Janeiro, Brazil; and Instituto Nacional de Neurociência Translacional, Instituto Nacional de Ciência e Tecnologia/Ministério de Ciência e Tecnologia, 04023-900, Sao Paulo, Brazil
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-06-22
- Volume
- 109
- Issue
- supplement_1
- Pages
- 10661-10668
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3386878
- OpenAlex
- W1966084915
- Semantic Scholar
- 65f9d025887298541bab310b071635e4588d2b68
- Cited by
- 726
- References
- 100
- Citations by year
- 2026: 37 · 2025: 69 · 2024: 93 · 2023: 73 · 2022: 58 · 2021: 60 · 2020: 89 · 2019: 51 · 2018: 36 · 2017: 30 · 2016: 38 · 2015: 38 · 2014: 30 · 2013: 19 · 2012: 4
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Primate Behavior and Ecology
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- human brain, primate brain, brain scaling, brain size, brain energy metabolism, brain energy consumption, cerebral cortex, neuron number, glial cells, brain evolution, cognitive ability
- MeSH terms
- Animals, Brain, Biological Evolution, Humans, Nerve Net, Neuroglia, Neurons, Primates
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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