Modeling and simulation of electrodiffusion in dense reconstructions of cerebral tissue

TL;DR

This paper introduces a computational framework for modeling electrodiffusion in dense cerebral tissue geometries derived from electron microscopy, addressing numerical challenges in simulating neuronal activity.

Contribution

The work presents a novel framework combining realistic tissue geometries with electrodiffusion modeling, enabling detailed simulations of neuronal activity in complex brain structures.

Findings

Highlighting numerical challenges in electrodiffusion modeling

Demonstrating simulation of neuronal activity in realistic geometries

Providing insights into multiphysics simulation complexities

Abstract

Excitable tissue is fundamental to brain function, yet its study is complicated by extreme morphological complexity and the physiological processes governing its dynamics. Consequently, detailed computational modeling of this tissue represents a formidable task, requiring both efficient numerical methods and robust implementations. Meanwhile, efficient and robust methods for image segmentation and meshing are needed to provide realistic geometries for which numerical solutions are tractable. Here, we present a computational framework that models electrodiffusion in excitable cerebral tissue, together with realistic geometries generated from electron microscopy data. To demonstrate a possible application of the framework, we simulate electrodiffusive dynamics in cerebral tissue during neuronal activity. Our results and findings highlight the numerical and computational challenges associated with modeling and simulation of electrodiffusion and other multiphysics in dense reconstructions of cerebral tissue.