Adaptive Octree Meshes for Simulation of Extracellular Electrophysiology

Authors

Christopher BC Girard, Chapman UniversityFollow
Dong Song, University of Southern California,

Document Type

Article

Publication Date

9-29-2023

Abstract

Objective. The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations. Approach. This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly. Main results. In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes achieve accuracy comparable to high-resolution static meshes in an order of magnitude less time. Significance. The proposed simulation pipeline improves model scalability, allowing greater detail with fewer computational resources. The implementation is available as an open-source Python module, providing flexibility and ease of reuse for the broader research community.

Comments

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Neural Engineering, volume 20, in 2023 following peer review. This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1088/1741-2552/acfabf.

The Creative Commons license below applies only to this version of the article.

Recommended Citation

Christopher B C Girard and Dong Song 2023 J. Neural Eng. 20 056028. https://doi.org/10.1088/1741-2552/acfabf

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.