ArticleFrontiers in computational neuroscience2018
Recording Neural Activity Based on Surface Plasmon Resonance by Optical Fibers-A Computational Analysis.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Optical Detection of Cellular Signals at Material Interfaces.Advanced healthcare materials · 2026Review
- Biosensors for Epilepsy Management: State-of-Art and Future Aspects.Sensors (Basel, Switzerland) · 2019Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An all optical, non-destructive method for monitoring neural activity has been proposed and its performance in detection has been analyzed computationally. The proposed method is based on excitation of Surface Plasmon Resonance (SPR) through the structure of optical fibers. The sensor structure consists of a multimode optical fiber where, the cladding of fiber has been removed and thin film of gold structure has been deposited on the surface. Impinging the laser light with appropriate wavelength inside the fiber and based on the total internal reflection, the evanescent wave will excite surface plasmons in the gold thin film. The absorption of light by surface plasmons in the gold structure is severely dependent on the dielectric properties at its vicinity. The electrical activity of neural cells (action potential) can modulate the dielectric properties at its vicinity and hence can modify the absorption of light inside the optical fiber. We have computationally analyzed the performance of the proposed sensor with different available geometries using Finite Element Method (FEM). In this regard, we have shown that the optical response of proposed sensor will track the action potential of the neuron at its vicinity. Based on different geometrical structure, the sensor has absorption in different regions of visible spectrum.
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Registered trials
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