Modelling extracellular electrical stimulation: IV. Effect of the cellular composition of neural tissue on its spatio-temporal filtering properties
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OBJECTIVE: The objective of this paper is to present a concrete application of the cellular composite model for calculating the membrane potential, described in an accompanying paper. APPROACH: A composite model that is used to determine the membrane potential for both longitudinal and transverse modes of stimulation is demonstrated. MAIN RESULTS: Two extreme limits of the model, near-field and far-field for an electrode close to or distant from a neuron, respectively, are derived in this paper. Results for typical neural tissue are compared using the composite, near-field and far-field models as well as the standard isotropic volume conductor model. The self-consistency of the composite model, its spatial profile response and the extracellular potential time behaviour are presented. The magnitudes of the longitudinal and transverse components for different values of electrode-neurite separations are compared. SIGNIFICANCE: The unique features of the composite model and its simplified versions can be used to accurately estimate the spatio-temporal response of neural tissue to extracellular electrical stimulation.
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The diversity of phosphene shapes is likely due to epiretinal stimulation activating both cell bodies beneath the electrode in the ganglion cell layer and axon bundles passing over the retinal surface in the nerve fiber layer toward the optic nerve.
“It is likely that there are a variety of phosphene shapes because stimulating through an epiretinal electrode not only activates the cell bodies that are just beneath the electrode in the ganglion cell layer, but it also activates axon bundles that pass over the surface of the retina in the nerve fiber layer while en route to the optic nerve (Beyeler et al. 2019; Esler et al. 2018; Tahayori et al.”
From Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only (2022) · cites this paper as Tahayori et al. 2014
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References 51 · Cited by 24
- Authors
- Tahayori, B., Meffin, H., Sergeev, E. N., Mareels, I. M. Y., Burkitt, A. N., & Grayden, D. B.
- Affiliations
- The University of Melbourne (AU)
- Bionics Institute (AU)
- Corresponding author
- Tahayori, Bahman
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-11-24
- Volume
- 11
- Issue
- 6
- Pages
- 065005
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W1974696329
- Cited by
- 24
- References
- 51
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 2 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 9 · 2017: 1 · 2016: 3 · 2015: 1 · 2014: 2
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Victorian Life Sciences Computation Initiative (VR0138)
- Australian Research Council (SR1000005)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Keywords
- extracellular electrical stimulation, nervous tissue, membrane potential, spatio-temporal filtering
- MeSH terms
- Cell Membrane, Electric Stimulation, Membrane Potentials, Models, Neurological, Nerve Tissue, Time Factors, Extracellular Fluid
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