Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

TL;DR

This paper develops a data-driven modeling approach to simulate cortical slow waves in the mouse brain, matching experimental recordings and enhancing understanding of brain dynamics.

Contribution

It introduces a two-step inference method combining likelihood maximization and neuro-modulation optimization for realistic cortical wave simulations.

Findings

The model reproduces key features of in-vivo cortical wave dynamics.

Simulations match experimental data in spatio-temporal features.

The approach aids understanding of brain states and neuromodulation effects.

Abstract

Thanks to novel, powerful brain activity recording techniques, we can create data-driven models from thousands of recording channels and large portions of the cortex, which can improve our understanding of brain-states neuromodulation and the related richness of traveling waves dynamics. We investigate the inference of data-driven models and the comparison among experiments and simulations, through the characterization of the spatio-temporal features of cortical waves in experimental recordings and simulations. Inference is built in two steps: the inner loop that optimizes by likelihood maximization a mean-field model, and the outer loop that optimizes a periodic neuro-modulation by relying on direct comparison of observables apt for the characterization of cortical slow waves. The model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the high-resolution recording of the in-vivo mouse brain obtained by wide-field calcium imaging techniques. The proposed approach is of interest for both experimental and computational neuroscientists.