On the evolution and geometry of the brain in mammals

Hofman, Michel A.

doi:10.1016/0301-0082(89)90013-0

Cited by 283 publications

(165 citation statements)

References 62 publications

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“…The scaling relationship between brain size and body mass in primates (and mammals generally) has also been a major topic of debate, in part because the influence of body mass must be considered in comparative analyses of brain evolution (e.g. Jerison, 1973;Martin, 1981;Hofman, 1989;Allman, 1999). However, a thorough understanding of brain-body allometry is impeded by numerous factors (reviewed in Deacon, 1990), including grade differences between primate clades.…”

Section: Introductionmentioning

confidence: 99%

Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set

Isler

Kirk

Miller

et al. 2008

Journal of Human Evolution

272

289

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We present a compilation of endocranial volumes (ECV) for 176 non-human primate species, based on individual data collected from 3813 museum specimens, at least 88% being wild-caught. In combination with body mass data from wild individuals, strong correlations between endocranial volume and body mass within taxonomic groups were found. Errors attributable to different techniques for measuring cranial capacity were negligible and unbiased. The overall slopes for regressions of log ECV on log body mass in primates are 0.773 for least-squares regression and 0.793 for reduced major axis regression. The leastsquares slope is reduced to 0.565 when independent contrasts are substituted for species means (branch lengths from molecular studies). A common slope of 0.646 is obtained with logged species means when grade shifts between major groups are taken into account using ANCOVA. In addition to providing a comprehensive and reliable database for comparative analyses of primate brain size, we show that the scaling relationship between brain mass and ECV does not differ significantly from isometry in primates. We also demonstrate that ECV does not differ substantially between captive and wild samples of the same species. ECV may be a more reliable indicator of brain size than brain mass, because considerably larger samples can be collected to better represent the full range of intraspecific variation. We also provide support for the maternal energy hypothesis by showing that BMR and gestation period are both positively correlated with brain size in primates, after controlling for the influence of body mass and potential effects of phylogenetic relatedness.

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Section: Introductionmentioning

confidence: 99%

Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set

Isler

Kirk

Miller

et al. 2008

Journal of Human Evolution

272

289

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“…allometry ͉ brain size ͉ primates ͉ number of neurons ͉ cortical surface T he expansion of a cerebral cortex that varies in surface area by more than five orders of magnitude across mammals (1) is considered a key event in mammalian brain evolution (2,3), even though evolution is not always associated with increased brain or cortical size (4). Given that the cerebral cortex is a columnar structure (2,5,6), the most accepted view of cortical expansion proposes that this is achieved through the addition of columnar modules (7,8).…”

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confidence: 99%

The basic nonuniformity of the cerebral cortex

Herculano‐Houzel

Collins²,

Wong³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

147

129

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These results have since been either corroborated or disputed by different groups. Here, we show that the number of neurons underneath 1 mm 2 of the cerebral cortical surface of nine primate species and the closely related Tupaia sp. is not constant and varies by three times across species. We found that cortical thickness is not inversely proportional to neuronal density across species and that total cortical surface area increases more slowly than, rather than linearly with, the number of neurons underneath it. The number of neurons beneath a unit area of cortical surface varies linearly with neuronal density, a parameter that is neither related to cortical size nor total number of neurons. Our finding of a variable number of neurons underneath a unit area of the cerebral cortex across primate species indicates that models of cortical organization cannot assume that cortical columns in different primates consist of invariant numbers of neurons.allometry ͉ brain size ͉ primates ͉ number of neurons ͉ cortical surface

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“…This is very close to the exponent of 1.138 for the total number of highway exits (Table 1b), suggesting perhaps that 9/8 may be the theoretical exponent for exits (as well as for number of zip codes and number of public high schools). As was the case for city highway networks, the surface density of synapses rises approximately as the 1/8 power of neocortex surface area, and this is entirely accommodated by the thickness of gray matter increasing as the 1/8 power [4,5,[13][14][15][16], amounting to a slow increase from about half a millimeter in the smallest mammals to a couple millimeters in man. Gray matter thickness may, then, be akin to the surface density of a city (some of this surface density increase in cities which may literally be due to increasing ''thickness,'' i.e., to cities growing in the third dimension).…”

Section: Number Of Highway Exits and Number Of Neuronal Synapsesmentioning

confidence: 99%

“…Neocortex white matter volume scales as approximately the 3/2 power of total convoluted surface area (with exponents ranging from about 1.4 to 1.52, Table 1) [3][4][5][6][7][8][9]. The most straightforward analog of white matter volume for city highway networks would be the entire volume utilized by highways, but we do not currently possess data for highway ''depth,'' and cannot calculate volume (we do not know, for example, if highway depth scales in the same manner as width).…”

Section: Total Highway System Surface Area and Total Surface Area Of mentioning

confidence: 99%

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confidence: 99%

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Common scaling laws for city highway systems and the mammalian neocortex

Changizi

Destefano

2009

Complexity

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A variety of scaling laws are known for the mammalian neocortex relating gray matter volume, total number of synapses [1,2], white matter volume [3][4][5][6][7][8][9], number of neurons [6,[10][11][12], surface area [4,5,[13][14][15][16], axon caliber [1,17], and number of cortical areas or compartments [18]. These neocortical scaling laws appear to be a consequence of selection pressure for a sheet-like structure (namely, gray matter) to economically maintain a high level of interconnectedness [1,2,19,20]. We might therefore expect to find similar scaling laws for any sheetlike structure under similar selection pressures, in which case the neocortex would be just an instance of a more general kind of structure.Here, we investigate city highway systems as a potential kind of network which may be driven by similar principles as the neocortex. In contrast to neurons, which are conduits for information-related signals on which brain computations rely, highways are conduits for physical materials and people. But from the perspective of the city as a whole, the materials and people that highways transport are crucial to the large-scale function carried out by the city, and are, in a sense, signals-that one signal is electric and the other physical may not matter in regards to the fundamental principles governing them. In addition to the prima facie analogy between city highway networks and the brain's neural connections, there are several other reasons we chose to examine city highway networks. First, the organization of city highway networks tends to be driven by political and economic forces over decades, rather than being planned in advanced following known principles of highway engineering [21]-i.e., city highways systems are a result of an evolution-like mechanism. Second, cities are under selection pressure to efficiently interconnect via highways and roads. Third, because cities lie on the land they are approximately a surface, or a sheet. Fourth, highway network data are readily available (and our data set consists of 60 U.S. cities varying in population from 10 4 to nearly 10 7 , see Table 2 for raw data). Finally, the organization of city highway systems is interesting in and of itself: nearly half of Earth's 6.6 billion people now live in cities, and cities are becoming ever larger and densely populated. The proper functioning of a city requires that people and materials be quickly moved throughout it. This is a very difficult design problem, one that is magnified by the fact that city population tends to grow much faster than city surface area, and cities tend to increase in population over time, so that the efficient highway network must constantly evolve from the pre-existing one. Identification of scaling laws governing how highway organization scales with city size could potentially lead to better highway systems, and more efficiently running cities.

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On the evolution and geometry of the brain in mammals

Cited by 283 publications

References 62 publications

Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set

Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set

The basic nonuniformity of the cerebral cortex

Common scaling laws for city highway systems and the mammalian neocortex

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