How the Cortex Gets Its Folds: An Inside-Out, Connectivity-Driven Model for the Scaling of Mammalian Cortical Folding

Larger mammalian cerebral cortices tend to have increasingly folded surfaces, often considered to result from the lateral expansion of the gray matter (GM), which, in a volume constrained by the cranium, causes mechanical compression that is relieved by inward folding of the white matter (WM), or to result from differential expansion of cortical layers. Across species, thinner cortices, presumably more pliable, would offer less resistance and hence become more folded than thicker cortices of a same size. However, such models do not acknowledge evidence in favor of a tension-based pull onto the GM from the inside, holding it in place even when the constraint imposed by the cranium is removed. Here we propose a testable, quantitative model of cortical folding driven by tension along the length of axons in the WM that assumes that connections through the WM are formed early in development, at the same time as the GM becomes folded, and considers that axonal connections through the WM generate tension that leads to inward folding of the WM surface, which pulls the GM surface inward. As an important necessary simplifying hypothesis, we assume that axons leaving or entering the WM do so approximately perpendicularly to the WM–GM interface. Cortical folding is thus driven by WM connectivity, and is a function of the fraction of cortical neurons connected through the WM, the average length, and the average cross-sectional area of the axons in the WM. Our model predicts that the different scaling of cortical folding across mammalian orders corresponds to different combinations of scaling of connectivity, axonal cross-sectional area, and tension along WM axons, instead of being a simple function of the number of GM neurons. Our model also explains variations in average cortical thickness as a result of the factors that lead to cortical folding, rather than as a determinant of folding; predicts that for a same tension, folding increases with connectivity through the WM and increased axonal cross-section; and that, for a same number of neurons, higher connectivity through the WM leads to a higher degree of folding as well as an on average thinner GM across species

The Human Brain in Numbers: A Linearly Scaled-up Primate Brain

The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10× more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain

Coordinated Scaling of Cortical and Cerebellar Numbers of Neurons

While larger brains possess concertedly larger cerebral cortices and cerebella, the relative size of the cerebral cortex increases with brain size, but relative cerebellar size does not. In the absence of data on numbers of neurons in these structures, this discrepancy has been used to dispute the hypothesis that the cerebral cortex and cerebellum function and have evolved in concert and to support a trend towards neocorticalization in evolution. However, the rationale for interpreting changes in absolute and relative size of the cerebral cortex and cerebellum relies on the assumption that they reflect absolute and relative numbers of neurons in these structures across all species – an assumption that our recent studies have shown to be flawed. Here I show for the first time that the numbers of neurons in the cerebral cortex and cerebellum are directly correlated across 19 mammalian species of four different orders, including humans, and increase concertedly in a similar fashion both within and across the orders Eulipotyphla (Insectivora), Rodentia, Scandentia and Primata, such that on average a ratio of 3.6 neurons in the cerebellum to every neuron in the cerebral cortex is maintained across species. This coordinated scaling of cortical and cerebellar numbers of neurons provides direct evidence in favor of concerted function, scaling and evolution of these brain structures, and suggests that the common notion that equates cognitive advancement with neocortical expansion should be revisited to consider in its stead the coordinated scaling of neocortex and cerebellum as a functional ensemble

L'évaluation du RMI : la simplification de la démarche et ses enjeux

URL des Cahiers : https://halshs.archives-ouvertes.fr/CAHIERS-MSECahiers de la MSE 2005.38 - Série Rouge - ISSN : 1624-0340This paper analyses the different evaluation programs which have been conducted since the beginning of the minimum income policy in 1989. It's aimed at interpreting the links between the evaluation models and the policies. Two approaches can be highlighted; the first one, that we can all "pluralistic approach" started with the early works of the national evaluation committee until the mid-90's and combined many purposes and procedures. Despite the extensive knowledge gained about the problems of poverty, it led to very few public decisions. The second approach initiated in the mid-90's resulted from the rising cost of the policy of minimum income due to the increasing number of beneficiaries. By focusing on simple indicators of efficiency and using econometric methods, the analysis appears more restrictive but it led to policy decisions which deeply modified the minimum income policy.L'analyse des travaux d'évaluation du RMI permet de distinguer deux approches principales. La première, pluraliste dans ses objectifs et ses méthodes, s'étend des travaux de la commission nationale d'évaluation jusqu'au milieu des années 90. En dépit de ses apports réels concernant la connaissance des phénomènes de pauvreté, cette approche a eu peu d'incidences pratiques. La seconde conception qui se développe à partir du milieu des années 90 est liée aux préoccupations financières engendrées par l'augmentation importante du nombre de bénéficiaires. En privilégiant des indicateurs simples d'efficacité et des méthodes issues de l'économétrie, elle apparaît plus restrictive dans ses investigations. Cependant, ses préconisations opérationnelles en termes d'action publique ont contribué à modifier le dispositif initial, en esquivant la question centrale de la pluralité de ses fonctions

Basic language learning in artificial animals

We explore a general architecture for artificial animals, or animats, that develops over time. The architecture combines reinforcementlearning, dynamic concept formation, and homeostatic decision-making aimed at need satisfaction. We show that thisarchitecture, which contains no ad hoc features for language processing, is capable of basic language learning of three kinds: (i)learning to reproduce phonemes that are perceived in the environment via motor babbling; (ii) learning to reproduce sequences ofphonemes corresponding to spoken words perceived in the environment; and (iii) learning to ground the semantics of spoken wordsin sensory experience by associating spoken words (e.g. the word “cold”) to sensory experience (e.g. the activity of a sensor forcold temperature) and vice versa

Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution