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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.”
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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“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
Flagged first, then by citations
Research works printed but not linked: 1
- GC Conroy Primate Evolution (WW Norton, New York, 1990).
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The Influence of Intestinal Microbiota on BDNF Levels
Molska, M., Mruczyk, K., Cisek-Woźniak, A., Prokopowicz, W., Szydełko, P., Jakuszewska, Z., Marzec, K., & Trocholepsza, M. (2024) · Nutrients · doi:10.3390/nu16172891 · Cited by 64
Ávila, J., Llorens-Martı́n, M., Pallas‐Bazarra, N., Bolós, M., Perea, J. R., Rodríguez-Matellán, A., & Hernández, F. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00626 · Cited by 61
Impact of glucose metabolism on the developing brain
Cacciatore, M., Grasso, E. A., Tripodi, R., & Chiarelli, F. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1047545 · Cited by 72
Glucose Requirements of the Developing Human Brain
Goyal, M. S., & Raichle, M. E. (2018) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000001875 · Cited by 66
Invariant Synapse Density and Neuronal Connectivity Scaling in Primate Neocortical Evolution
Sherwood, C. C., Miller, S. B., Karl, M. T., Stimpson, C. D., Phillips, K. A., Jacobs, B., Hof, P. R., Raghanti, M. A., & Smaers, J. B. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa149 · Cited by 60
Heterogeneity and Efficiency in the Brain
Balasubramanian, V. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2447016 · Cited by 58
Graphene-based neurotechnologies for advanced neural interfaces
Lu, Y., Liu, X., & Kuzum, D. (2018) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2018.06.001 · Cited by 55
The Evolution of Primate Executive Function: From Response Control to Strategic Decision-Making
Rosati, A. G. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00093-2 · Cited by 56
Kim, S., Zhang, H., & Rubio‐Magnieto, J. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c02170 · Cited by 57
Seymour, R. S., Bosiocic, V., & Snelling, E. P. (2016) · Royal Society Open Science · doi:10.1098/rsos.160305 · Cited by 58
Bretas, R. V., Taoka, M., Suzuki, H., & Iriki, A. (2020) · Experimental Brain Research · doi:10.1007/s00221-020-05727-9 · Cited by 61
Scale-free networks with exponent one
Timár, G., Dorogovtsev, S. N., & Mendes, J. F. F. (2016) · Physical review. E · doi:10.1103/physreve.94.022302 · Cited by 55
Diagnosis of early Alzheimer’s disease based on dynamic high order networks
Lei, B., Yu, S., Zhao, X., Frangi, A. F., Tan, E.-L., Elazab, A., Wang, T., & Wang, S. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00255-9 · Cited by 58
The Cajal school and the physiological role of astrocytes: a way of thinking
Navarrete, M. (2014) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2014.00033 · Cited by 60
Bowman, F. D. (2014) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115611 · Cited by 55
The Quantitative Comparison Between the Neuronal Network and the Cosmic Web
Vazza, F., & Feletti, A. (2020) · Frontiers in Physics · doi:10.3389/fphy.2020.525731 · Cited by 55
A ventral salience network in the macaque brain
Touroutoglou, A., Bliss‐Moreau, E., Zhang, J., Mantini, D., Vanduffel, W., Dickerson, B. C., & Barrett, L. F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.029 · Cited by 54
Sah, E., Krishnamurthy, S., Ahmidouch, M. Y., Gillispie, G. J., Milligan, C., & Orr, M. E. (2021) · Life · doi:10.3390/life11030229 · Cited by 55
The Molecular Basis of Human Brain Evolution
Enard, W. (2016) · Current Biology · doi:10.1016/j.cub.2016.09.030 · Cited by 55
The exercise pressor reflex: An update
Teixeira, A. L. D. S., & Vianna, L. C. (2022) · Clinical Autonomic Research · doi:10.1007/s10286-022-00872-3 · Cited by 54
Mastaitis, J. W., Gomez, D., Raya, J. G., Li, D., Min, S., Stec, M., Kleiner, S., McWilliams, T., Altarejos, J. Y., Murphy, A. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59485-9 · Cited by 53
Adebimpe, A., Bertolero, M., Dolui, S., Cieslak, M., Murtha, K., Baller, E. B., Boeve, B., Boxer, A., Butler, E. R., Cook, P. A., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01458-7 · Cited by 43
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Sasaki, S., Futagi, Y., Kobayashi, M., Ogura, J., & Iseki, K. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.581892 · Cited by 53
Brisson, C. D., Lukewich, M. K., & Andrew, R. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079589 · Cited by 53
Vascularized human brain organoid on-chip
Tan, S. Y., Feng, X., Cheng, L. K., & Wu, A. R. (2023) · Lab on a Chip · doi:10.1039/d2lc01109c · Cited by 52
Unraveling mechanisms of human brain evolution
Lancaster, M. A. (2024) · Cell · doi:10.1016/j.cell.2024.08.052 · Cited by 58
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Xiao, Y., Gao, C., Jin, J., Sun, W., Wang, B., Bao, Y., Liu, C., Huang, W., Zeng, H., & Yu, Y. (2024) · Advanced Devices & Instrumentation · doi:10.34133/adi.0044 · Cited by 61
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Maksimovic, K., Youssef, M., You, J., Sung, H., & Park, J. (2023) · Biomolecules · doi:10.3390/biom13050863 · Cited by 51
Somatic mutations in the human brain: implications for psychiatric research
Nishioka, M., Bundo, M., Iwamoto, K., & Kato, T. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0129-y · Cited by 47
Toward Reflective Spiking Neural Networks Exploiting Memristive Devices
Makarov, V. A., Lobov, S. A., Shchanikov, S., Mikhaylov, A. N., & Kazantsev, V. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.859874 · Cited by 48
The technology, opportunities, and challenges of Synthetic Biological Intelligence
Kagan, B. J., Gyngell, C., Lysaght, T., Cole, V. M., Sawai, T., & Savulescu, J. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108233 · Cited by 50
Transcriptomic insights into human brain evolution: acceleration, neutrality, heterochrony
Somel, M., Rohlfs, R. V., & Liu, X. (2014) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2014.09.001 · Cited by 49
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Zollikofer, C. P. E., Bienvenu, T., & de León, M. S. P. (2016) · Journal of Anatomy · doi:10.1111/joa.12531 · Cited by 47
Gomez, J., Zhen, Z., & Weiner, K. S. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000362 · Cited by 49
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Weichwald, S., & Peters, J. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01623 · Cited by 51
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Buil, B. A., Tackenberg, C., & Rust, R. (2022) · Brain · doi:10.1093/brain/awac393 · Cited by 47
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Adamo, S. A. (2019) · The Canadian Entomologist · doi:10.4039/tce.2019.49 · Cited by 52
Toward the next generation of vascularized human neural organoids
Li, M., Gao, L., Zhao, L., Zou, T., & Xu, H. (2022) · Medicinal Research Reviews · doi:10.1002/med.21922 · Cited by 45
Brain-Inspired Remote Sensing Foundation Models and Open Problems: A Comprehensive Survey
Jiao, L., Huang, Z., Lu, X., Liu, X., Yang, Y., Zhao, J., Zhang, J., Hou, B., Yang, S., Liu, F., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3316302 · Cited by 49
Developmental heterochrony and the evolution of autistic perception, cognition and behavior
Crespi, B. J. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-119 · Cited by 51
Extraordinary intelligence and the care of infants
Piantadosi, S. T., & Kidd, C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506752113 · Cited by 45
Honegger, T., Thielen, M., Feizi, S., Sanjana, N. E., & Joel Voldman, B. (2016) · Scientific Reports · doi:10.1038/srep28384 · Cited by 44
Genomic trade-offs: are autism and schizophrenia the steep price of the human brain?
Sikela, J. M., & Quick, V. B. S. (2018) · Human Genetics · doi:10.1007/s00439-017-1865-9 · Cited by 44
Arafat, E. A.-G., & Shabaan, D. A. (2019) · Acta Histochemica · doi:10.1016/j.acthis.2019.08.002 · Cited by 43
High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility
Sneve, M. H., Grydeland, H., Rosa, M. G. P., Paus, T., Chaplin, T. A., Walhovd, K. B., & Fjell, A. M. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy268 · Cited by 41
Evidence for independent brain and neurocranial reorganization during hominin evolution
Warren, J. L. A., de León, M. S. P., Hopkins, W. D., & Zollikofer, C. P. E. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905071116 · Cited by 45
Can neurofeedback provide evidence of direct brain-behavior causality?
Kvamme, T. L., Ros, T., & Overgaard, M. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119400 · Cited by 46
Shamul, J. G., Wang, Z., Gong, H., Ou, W., White, A. M., Garcia, D. P. M., Gu, S., Clyne, A. M., Quiñones‐Hinojosa, A., & He, X. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01250-2 · Cited by 43
Silver, D. L. (2015) · BioEssays · doi:10.1002/bies.201500108 · Cited by 41
No evidence for general intelligence in a fish
Aellen, M., Burkart, J. M., & Bshary, R. (2022) · Ethology · doi:10.1111/eth.13275 · Cited by 43
Freas, C. A., Roth, T. C., LaDage, L. D., & Витальевич, П. В. (2013) · Functional Ecology · doi:10.1111/1365-2435.12125 · Cited by 40
Neurogenesis in primates versus rodents and the value of non-human primate models
Zhang, R., Quan, H., Wang, Y., & Luo, F. (2023) · National Science Review · doi:10.1093/nsr/nwad248 · Cited by 47
Bayesian Mindsponge FrameworkPreprint
Nguyen, M., & Le, T.-T. (2021) · doi:10.31219/osf.io/b4uny · Cited by 39
Evolution of cortical geometry and its link to function, behaviour and ecology
Schwartz, E., Nenning, K., Heuer, K., Jeffery, N. S., Bertrand, O., Toro, R., Kasprian, G. J., Prayer, D., & Langs, G. (2023) · Nature Communications · doi:10.1038/s41467-023-37574-x · Cited by 40
Molecular neuroanatomy: a generation of progress
Pollock, J. D., Wu, D.-Y., & Satterlee, J. S. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2013.11.001 · Cited by 36
Freas, C. A., Bingman, K., LaDage, L. D., & Витальевич, П. В. (2013) · Brain Behavior and Evolution · doi:10.1159/000355503 · Cited by 37
High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons
Tyukin, I., Gorban, A. N., Calvo, C., Makarova, J., & Makarov, V. A. (2018) · Figshare · Cited by 36
Ilieş, I., Muscedere, M. L., & Traniello, J. F. A. (2015) · Brain Behavior and Evolution · doi:10.1159/000370100 · Cited by 34
Keilholz, S. D., Pan, W., Billings, J., Nezafati, M., & Shakil, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.019 · Cited by 39
Zhao, G., Walsh, K., Long, J., Gui, W., & Denisova, K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196964 · Cited by 34
Pinto, J. G. A., Jones, D. G., Williams, C. K., & Murphy, K. M. (2015) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00003 · Cited by 37
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 40
Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model
Panda, P., & Srinivasa, N. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00126 · Cited by 33
Reverse engineering human brain evolution using organoid models
Mostajo-Radji, M. A., Schmitz, M. T., Torres, S., & Pollen, A. A. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146582 · Cited by 36
Genomic Mosaicism Formed by Somatic Variation in the Aging and Diseased Brain
Costantino, I. M., Nicodemus, J., & Chun, J. J. M. (2021) · Genes · doi:10.3390/genes12071071 · Cited by 34
Stuttering: A Disorder of Energy Supply to Neurons?
Alm, P. A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.662204 · Cited by 35
A Comparative Perspective on the Cerebello-Cerebral System and Its Link to Cognition
Magielse, N., Heuer, K., Toro, R., Schutter, D. J. L. G., & Valk, S. L. (2022) · The Cerebellum · doi:10.1007/s12311-022-01495-0 · Cited by 36
Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fatPreprint
Arseniev‐Koehler, A., & Foster, J. G. (2020) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/c9yj3 · Cited by 31
Duong, A., Evstratova, A., Sivitilli, A., Hernández, J. J., Gosio, J., Wahedi, A., Sondheimer, N., Wrana, J. L., Beaulieu, J., Attisano, L., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-84071-6 · Cited by 34
High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons
Tyukin, I., Gorban, A. N., Calvo, C., Makarova, J., & Makarov, V. A. (2018) · Bulletin of Mathematical Biology · doi:10.1007/s11538-018-0415-5 · Cited by 39
Guardiola-Ripoll, M., & Fatjó‐Vilas, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043597 · Cited by 32
Geerts, H., Bergeler, S., Walker, M., van der Graaf, P. H., & Courade, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-41382-0 · Cited by 32
Zebrafishology, study design guidelines for rigorous and reproducible data using zebrafish
Bedell, V. M., Dubey, P., Lee, H. B., Bailey, D. S., Anderson, J. L., Jamieson-Lucy, A., Xiao, R., Leonard, E. V., Falk, M. J., Pack, M. A., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-07496-z · Cited by 36
First Bites—Why, When, and What Solid Foods to Feed Infants
Borowitz, S. M. (2021) · Frontiers in Pediatrics · doi:10.3389/fped.2021.654171 · Cited by 31
Knowledge production and world population dynamics
Dolgonosov, B. M. (2015) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2015.10.023 · Cited by 30
Topological disintegration of resting state functional connectomes in coma
Malagurski, B., Péran, P., Sarton, B., Vinour, H., Naboulsi, E., Riu, B., Bounes, F. V., Seguin, T., Lotterie, J. A., Fourcade, O. A., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.012 · Cited by 34
The Brains of Reptiles and Birds
Güntürkün, O., Stacho, M., & Ströckens, F. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00007-5 · Cited by 32
Island Rule, quantitative genetics and brain–body size evolution inHomo floresiensis
Diniz‐Filho, J. A. F., & Raia, P. (2017) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2017.1065 · Cited by 29
Implicit and explicit evaluations of foods: The natural and transformed dimension
Coricelli, C., Foroni, F., Osimo, S. A., & Rumiati, R. I. (2018) · Food Quality and Preference · doi:10.1016/j.foodqual.2018.11.014 · Cited by 31
Wang, P., Liang, Y., Chen, K., Yau, S., Sun, X., Cheng, K. K. Y., Xu, A., So, K., & Li, A. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.00189 · Cited by 29
Differential contribution of transcriptomic regulatory layers in the definition of neuronal identity
Ha, K., Sterne-Weiler, T., Morris, Q., Weatheritt, R. J., & Blencowe, B. J. (2021) · Nature Communications · doi:10.1038/s41467-020-20483-8 · Cited by 29
Autonomous Robots and the SP Theory of Intelligence
Wolff, J. G. (2014) · IEEE Access · doi:10.1109/access.2014.2382753 · Cited by 29
Brain Plasticity and Experience
Kolb, B. E. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804036-2.00013-3 · Cited by 31
Kim, E., Figueiredo, I., Simmons, C., Randall, K., Gonzalez, L. R., Wood, T., Ranieri, B., Sureda-Gibert, P., Howes, O. D., Pariante, C. M., et al. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.07.010 · Cited by 28
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Xiong, G.-J., & Sheng, Z. (2024) · The Journal of Cell Biology · doi:10.1083/jcb.202401145 · Cited by 29
Distinct brain representations of processed and unprocessed foods
Coricelli, C., Toepel, U., Notter-Bielser, M.-L., Murray, M. M., & Rumiati, R. I. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14498 · Cited by 28
Fritzsch, B., Jahan, I., Pan, N., & Elliott, K. L. (2014) · Cell and Tissue Research · doi:10.1007/s00441-014-2043-1 · Cited by 29
Hagen, E. H., & Garfield, Z. H. (2019) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/9bcdk · Cited by 28
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Ziats, M. N., Grosvenor, L. P., & Rennert, O. M. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.153 · Cited by 27
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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
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