University of Central Florida
Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface.
Degree: 2012, University of Central Florida
Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode…
Advisors/Committee Members: Hickman, James.
Subjects/Keywords: Whole cell biosensors; cell sensor interface; neuron electrode interface; neuroelectronic interfacing; microelectrode arrays; meas; field effect transistor (fet) arrays; limitations of equivalent circuit models; volterra wiener modeling; nonlinear dynamic modeling; nonlinear impedance spectroscopy; lattice boltzmann method; lattice poisson boltzmann method; multiphysics solver; entrance flow problem; surface chemical reaction; electroosmotic flow; ionic electrodiffusion; molecular speed dependent relaxation time; primitive model of electrolyte; charge relaxation dynamics; electrolytic nanocapacitor; effect of nanoscale confinement; overlapping electric double layers; electric double layer relaxation; transient charging dynamics of an electrolytic capacitor; anomalous plasma like collective oscillations; viscous drag force on ions in a solvent; synergistic modeling of the cell sensor interface; Physics; Dissertations, Academic – Sciences, Sciences – Dissertations, Academic
to Zotero / EndNote / Reference
APA (6th Edition):
Thakore, V. (2012). Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface. (Doctoral Dissertation). University of Central Florida. Retrieved from https://stars.library.ucf.edu/etd/2465
Chicago Manual of Style (16th Edition):
Thakore, Vaibhav. “Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface.” 2012. Doctoral Dissertation, University of Central Florida. Accessed December 10, 2019.
MLA Handbook (7th Edition):
Thakore, Vaibhav. “Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface.” 2012. Web. 10 Dec 2019.
Thakore V. Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface. [Internet] [Doctoral dissertation]. University of Central Florida; 2012. [cited 2019 Dec 10].
Available from: https://stars.library.ucf.edu/etd/2465.
Council of Science Editors:
Thakore V. Nonlinear Dynamic Modeling, Simulation And Characterization Of The Mesoscale Neuron-electrode Interface. [Doctoral Dissertation]. University of Central Florida; 2012. Available from: https://stars.library.ucf.edu/etd/2465