BibTeX

@article{2509.02799v2,
Author        = {Martin Breyton and Viktor Sip and Marmaduke Woodman and Meysam Hashemi and Spase Petkoski and Viktor Jirsa},
Title         = {Data-driven mean-field within whole-brain models},
Eprint        = {2509.02799v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {q-bio.NC},
Abstract      = {Mean-field models provide a link between microscopic neuronal activity and
macroscopic brain dynamics. Their derivation depends on simplifying
assumptions, such as all-to-all connectivity, limiting their biological
realism. To overcome this, we introduce a data-driven framework in which a
multi-layer perceptron (MLP) learns the macroscopic dynamics directly from
simulations of a network of spiking neurons. The network connection probability
serves here as a new parameter, inaccessible to purely analytical treatment,
which is validated against ground truth analytical solutions. Through
bifurcation analysis on the trained MLP, we demonstrate the existence of new
cusp bifurcation that systematically reshapes the system's phase diagram in a
degenerate manner with synaptic coupling. By integrating this data-driven
mean-field model into a whole-brain computational framework, we show that it
extends beyond the macroscopic emergent dynamics generated by the analytical
model. For validation, we use simulation-based inference on synthetic
functional magnetic resonance imaging (fMRI) data and demonstrate accurate
parameter recovery for the novel mean-field model, while the current
state-of-the-art models lead to biased estimates. This work presents a flexible
and generic framework for building more realistic whole-brain models, bridging
the gap between microscale mechanisms and macroscopic brain recordings.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.02799v2},
File          = {2509.02799v2.pdf}
}