Journal article
Jari Pronold, Alexander van Meegen, Hannah Vollenbröker, R. O. Shimoura, M. Senden, C. Hilgetag, Rembrandt Bakker, Sacha J. van Albada
bioRxiv, 2023
bioRxiv, 2023
Semantic Scholar
DOI
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APA
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Pronold, J., van Meegen, A., Vollenbröker, H., Shimoura, R. O., Senden, M., Hilgetag, C., … van Albada, S. J. (2023). Multi-Scale Spiking Network Model of Human Cerebral Cortex. BioRxiv.
Chicago/Turabian
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Pronold, Jari, Alexander van Meegen, Hannah Vollenbröker, R. O. Shimoura, M. Senden, C. Hilgetag, Rembrandt Bakker, and Sacha J. van Albada. “Multi-Scale Spiking Network Model of Human Cerebral Cortex.” bioRxiv (2023).
MLA
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Pronold, Jari, et al. “Multi-Scale Spiking Network Model of Human Cerebral Cortex.” BioRxiv, 2023.
BibTeX Click to copy
@article{jari2023a,
title = {Multi-Scale Spiking Network Model of Human Cerebral Cortex},
year = {2023},
journal = {bioRxiv},
author = {Pronold, Jari and van Meegen, Alexander and Vollenbröker, Hannah and Shimoura, R. O. and Senden, M. and Hilgetag, C. and Bakker, Rembrandt and van Albada, Sacha J.}
}
Abstract
Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand it. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a 1 mm2 column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and DTI, into a coherent framework. It predicts activity on all scales from single-neuron spiking activity to the area-level functional connectivity. We compared the model activity against human electrophysiological data and human resting-state fMRI data. This comparison reveals that the model can reproduce both spiking statistics and fMRI correlations if the cortico-cortical connections are sufficiently strong. Furthermore, we show that a single-spike perturbation propagates through the network within a time close to the limit imposed by the delays.
