: Cellular scaling rules for rodent brains.
Open accessAbstract
How do cell number and size determine brain size? Here, we show that, in the order Rodentia, increased size of the cerebral cortex, cerebellum, and remaining areas across six species is achieved through greater numbers of neurons of larger size, and much greater numbers of nonneuronal cells of roughly invariant size, such that the ratio between total neuronal and nonneuronal mass remains constant across species. Although relative cerebellar size remains stable among rodents, the number of cerebellar neurons increases with brain size more rapidly than in the cortex, such that the cerebellar fraction of total brain neurons increases with brain size. In contrast, although the relative cortical size increases with total brain size, the cortical fraction of total brain neurons remains constant. We propose that the faster increase in average neuronal size in the cerebral cortex than in the cerebellum as these structures gain neurons and the rapidly increasing glial numbers that generate glial mass to match total neuronal mass at a fixed glia/neuron total mass ratio are fundamental cellular constraints that lead to the relative expansion of cerebral cortical volume across species.
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The cerebral cortex varies in relative size from 42% in the mouse to 82% in the human, but contains between 13 and 28% of all brain neurons in 15 of 18 species studied, ranging from 13% in moles to 41% in the squirrel monkey.
“the cerebral cortex, despite varying in relative size from 42% (in the mouse) to 82% of brain mass (in the human), contains between 13 and 28% of all brain neurons in 15 of 18 species studied, ranging between 13% (in moles) and 41% (in the squirrel monkey; Herculano-Houzel et al., 2006, 2007; Sarko et al., 2009).”
From The human brain in numbers: a linearly scaled-up primate brain (2009) · cites this paper as Herculano-Houzel et al., 2006
The Results (page not provided) report that cortical mass increases with brain size while the cortex contains a relatively stable 17.8 ± 3.4% of brain neurons across six rodent species. This supports the general pattern, but the paper does not establish the quoted 42%–82% mass range or the neuron percentages across 18 species, including humans, moles, and squirrel monkeys. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn mammals, the pallium accounts for most of total brain volume, but the cerebellum houses a large majority of brain neurons.
“This markedly contrasts with the situation found in mammals, in which the pallium accounts for most of total brain volume, but the cerebellum houses a large majority of brain neurons (32–39)”
From Birds have primate-like numbers of neurons in the forebrain (2016) · cites this paper as [32]
The Results report that the cerebellum contains 59.0%–72.4% of brain neurons in the six rodent species studied, while the cerebral cortex becomes the dominant structure by volume as brain size increases. The source does not establish that the pallium occupies most brain volume across mammals generally, or even across all six rodents. The indexed passages have section labels but no page numbers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLike in mammals, the proportion of nonneuronal cells is very small in the cerebellum, varying between 12% and 19% in songbirds and between 14% and 19% in parrots, but, in contrast to mammals, nonneuronal cells also constitute a minor cellular fraction in the telencephalon, representing 21-40% of cells in songbirds and 31-43% of cells in parrots.
“Like in mammals (32–39, 46), the proportion of nonneuronal cells is very small in the cerebellum, varying between 12% and 19% in songbirds and between 14% and 19% in parrots, but, in contrast to mammals, nonneuronal cells also constitute a minor cellular fraction in the telencephalon, representing 21–40% of cells in songbirds and 31–43% of cells in parrots”
From Birds have primate-like numbers of neurons in the forebrain (2016) · cites this paper as [32]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIn Rodentia, brain size increases faster than neuron number, with neuronal density decreasing; with constant non-neuronal density, this implies average neuronal size increases rapidly as neurons become more numerous.
“In the order Rodentia, we find that the brain increases in size faster than it gains neurons, with a decrease in neuronal densities which, in the presence of constant non-neuronal cell densities, implies that average neuronal size increases rapidly as neurons become more numerous (Herculano-Houzel et al., 2006).”
From The human brain in numbers: a linearly scaled-up primate brain (2009) · cites this paper as Herculano-Houzel et al., 2006
The HiAER-Spike system capacity is roughly twice the neurons of a mouse brain and runs at faster than real time.
“In this work, we present HiAER-Spike, a modular, recon fi gurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time.”
From HiAER-spike software-hardware reconfigurable platform for event-driven neuromorphic computing at scale (2026) · cites this paper as [9]
Non-neuronal cell density is remarkably constant across these species.
“Given that non-neuronal cell density is remarkably constant across these species [24], [25]”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
There is no uniform scaling of neuronal density with brain size.
“However, it has now been demonstrated that there is no uniform scaling of neuronal density with brain size [24], [25], [41], which invalidates the previous estimate”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
Brain metabolism is not necessarily related to whole body metabolism in any determining way; any apparent relationship might be coincidental and dependent on the rate with which brain size scales as a function of its number of neurons.
“the scaling of brain metabolism as a function of total numbers of neurons opens the possibility that brain metabolism is not necessarily related to whole body metabolism in any determining way; any apparent relationship might be coincidental, and dependent on the rate with which brain size scales as a function of its number of neurons, which we have shown to vary across mammalian orders [24], [25]”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
Mammals whose brain scales with economical neuronal scaling rules, such as primates, have a large number of brain neurons for a given body size and would be expected to have a larger relative brain metabolic rate than other mammals, such as rodents, which have a smaller number of brain neurons for a same body or brain size.
“In this way, mammals whose brain scales with economical neuronal scaling rules, such as primates, have a large number of brain neurons for a given body size, and would accordingly be expected to have a larger relative brain metabolic rate than other mammals, such as rodents, which have a smaller number of brain neurons for a same body or brain size [24], [25].”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
The previous estimate that average energy use per neuron increases with brain size is invalidated.
“However, it has now been demonstrated that there is no uniform scaling of neuronal density with brain size [24], [25], [41], which invalidates the previous estimate”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
The human brain's use of 20% of the whole body energy budget while representing only 2% of body mass can be explained by the large number of neurons in the human brain, about 10× larger than would be expected of a rodent brain of its size.
“According to the scaling of brain metabolism with its total number of neurons proposed above, the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of this mass can be explained quite simply by the large number of neurons in the human brain, about 10× larger than would be expected of a rodent brain of its size [44], given the different”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
The glia/neuron ratio may be determined simply by the addition of glial cells of a relatively constant size to neuronal parenchyma consisting of small or large neurons during brain development.
“the glia/neuron ratio may be determined simply by the addition of glial cells of a relatively constant size to neuronal parenchyma consisting of small or large neurons during brain development [24], [43].”
From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [24]
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“…aim for each FPGA to support up to four million neurons and one billion synapses, yielding a total capacity of up to 160 million neurons and 40 billion synapses for the entire system - more than twice the number of neurons of a typical mouse brain [9] and at faster-than real-time simulation speed.”
- Authors
- Herculano-Houzel, S., Mota, B., & Lent, R.
- Affiliations
- *Departamento de Anatomia, Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, 21941-590, Rio de Janeiro, Brazil; and
- Centro Brasileiro de Pesquisas Físicas (BR)
- Universidade Federal do Rio de Janeiro (BR)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2006-08-08
- Volume
- 103
- Issue
- 32
- Pages
- 12138-12143
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC1567708
- OpenAlex
- W2166670234
- Cited by
- 579
- References
- 43
- Citations by year
- 2026: 27 · 2025: 29 · 2024: 58 · 2023: 51 · 2022: 36 · 2021: 51 · 2020: 64 · 2019: 26 · 2018: 31 · 2017: 22 · 2016: 29 · 2015: 19 · 2014: 28 · 2013: 21 · 2012: 18
- FWCI
- 3.67
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Universidade Federal do Rio de Janeiro
- Topics
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Social Sciences › Psychology › Social Psychology › Primate Behavior and Ecology
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Keywords
- brain scaling, rodent brain, cerebral cortex, cerebellum, neuron size, non-neuronal cells, brain size evolution, cross-species comparison, cortical expansion
- MeSH terms
- Animals, Brain, Cerebellum, Cerebral Cortex, Guinea Pigs, Male, Models, Neurological, Neurons, Rodentia, Species Specificity, Rats, Wistar, Mice, Rats
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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