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A Detailed Model for Understanding the Human Neocortex

Zulaica, Natali Barros
•
Kanari, Lida  
•
Sood, Vishal  
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January 29, 2026

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapt- ing data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 um, a height of 2622 um, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

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Type
preprint
DOI
10.64898/2026.01.29.702592
Author(s)
Zulaica, Natali Barros

École Polytechnique Fédérale de Lausanne

Kanari, Lida  

École Polytechnique Fédérale de Lausanne

Sood, Vishal  

École Polytechnique Fédérale de Lausanne

Rai, Pranav  

École Polytechnique Fédérale de Lausanne

Arnaudon, Alexis  

École Polytechnique Fédérale de Lausanne

Shi, Ying  

École Polytechnique Fédérale de Lausanne

Mange, Darshan

École Polytechnique Fédérale de Lausanne

Van Geit, Werner  

École Polytechnique Fédérale de Lausanne

Zbili, Michael

École Polytechnique Fédérale de Lausanne

Reva, Maria  

École Polytechnique Fédérale de Lausanne

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Date Issued

2026-01-29

Publisher

openRxiv

Written at

EPFL

EPFL units
EDNE-ENS  
LNMC  
FunderFunding(s)Grant NumberGrant URL

This study was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government s ETH Board of the Swiss Federal Institutes of Technology.

Available on Infoscience
February 2, 2026
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/258781
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