Modeling extracellular fields for a three-dimensional network of cells using NEURON.

Abstract

BACKGROUND Computational modeling of biological cells usually ignores their extracellular fields, assuming them to be inconsequential. Though such an assumption might be justified in certain cases, it is debatable for networks of tightly packed cells, such as in the central nervous system and the syncytial tissues of cardiac and smooth muscle. NEW METHOD In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment. The existing features of the simulator are extended by explicitly defining current balance equations, resulting in the coupling of the extracellular fields of adjacent cells. RESULTS With this technique, we achieved continuity of extracellular space for a network model, thereby allowing the exploration of extracellular interactions computationally. Using a three-dimensional network model, passive and active electrical properties were evaluated under varying levels of extracellular volumes. Simultaneous intracellular and extracellular recordings for synaptic and action potentials were analyzed, and the potential of ephaptic transmission towards functional coupling of cells was explored. COMPARISON WITH EXISTING METHOD(S) We have implemented a true bi-domain representation of a network of cells, with the extracellular domain being continuous throughout the entire model. This has hitherto not been achieved using NEURON, or other compartmental modeling platforms. CONCLUSIONS We have demonstrated the coupling of the extracellular field of every cell in a three-dimensional model to obtain a continuous uniform extracellular space. This technique provides a framework for the investigation of interactions in tightly packed networks of cells via their extracellular fields.

DOI: 10.1016/j.jneumeth.2017.07.005

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Cite this paper

@article{Appukuttan2017ModelingEF, title={Modeling extracellular fields for a three-dimensional network of cells using NEURON.}, author={Shailesh Appukuttan and Keith L. Brain and Rohit Manchanda}, journal={Journal of neuroscience methods}, year={2017}, volume={290}, pages={27-38} }