bioRxivBiophysicsPreprint
A Bidomain Boundary Element-Cable Method for Modeling Neuronal Responses to Electric Fields
Abstract
Objective: Extracellular electric fields critically influence neural activity through both exogenous neuromodulation and endogenous ephaptic coupling. While conventional cable models efficiently simulate membrane dynamics, they fail to capture bidirectional, field-mediated interactions self-consistently, and fully coupled volumetric methods require computationally prohibitive 3D meshing. We present Cable-BEM, a hybrid wire-kernel bidomain boundary element method designed to resolve these limitations. Approach: By analytically integrating boundary integral kernels around cylindrical neuronal compartments, Cable-BEM fully couples intracellular, extracellular, and membrane dynamics while strictly retaining the highly efficient 1D degrees of freedom of traditional cable equations. The system is advanced using a semi-implicit Crank-Nicolson scheme. To overcome the dense nature of the resulting integral operators, we implement an Adaptive Cross Approximation (ACA) and Hierarchical Off-Diagonal Low-Rank (HODLR) compression scheme. Main result: The solver was rigorously validated against full-surface bidomain boundary element method (BEM) reference implementations, demonstrating tight agreement in activation thresholds (within 1.3% relative error) across diverse stimulation geometries. The ACA-HODLR compression scheme achieved substantial memory footprint reductions-by a factor of up to 4.6 for large 225-cell networks- without sacrificing numerical accuracy. Furthermore, we utilized the framework to resolve subtle, distance-dependent ephaptic interactions, successfully demonstrating the progressive phase synchronization of biophysically realistic, multi-compartment Purkinje cells. Significance: Cable-BEM provides a computationally scalable, mesh-free framework that establishes a powerful and practical foundation for investigating complex field-mediated phenomena in large-scale, multicellular neuronal networks.
- assay: electrophysiology
- research focus: synaptic & neural circuits
- approach: deep learning
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A Boundary Element Method of Bidomain Modeling for Predicting Cellular Responses to Electromagnetic Fields
D. M. Czerwonky, A. S. Aberra, L. J. Gomez·bioRxiv·Dec 15, 2023
- Modeling Pyramidal Neurons Using Bidomain BEM and Hierarchical Matrix Approximation
Nahian I. Hasan, Vanine Sabino, … Luis J. Gomez·IEEE Journal of Electromagnetics RF and Microwaves in Medicine and BiologyPeer-reviewed·Jun 5, 2026
- Extracellular Stimulation and Ephaptic Coupling of Neurons in a Fully Coupled Finite Element-Based Extracellular-Membrane-Intracellular (EMI) Model
Karoline Horgmo Jæger, Aslak Tveito·Frontiers in Computational NeurosciencePeer-reviewed·Feb 12, 2026
- Microscopic and Macroscopic Response of a Cortical Neuron to an External Electric Field Computed with the Boundary Element Fast Multipole Method
Sergey N. Makarov, Gregory M. Noetscher, Padmavathi Sundaram·bioRxiv·Aug 13, 2018
- Neural bidomain model for multidimensional ephaptic coupling of neural spike propagation along myelinated fiber bundles with the nodes of Ranvier
Sehun Chun, Linyu Peng, Hae‐Jeong Park·bioRxiv·Dec 18, 2024
- Weak electric fields promote resonance in neuronal spiking activity: analytical results from two-compartment cell and network models
Josef Ladenbauer, Klaus Obermayer·PLoS Computational BiologyPeer-reviewed·Apr 22, 2019