Title

Comparative Analysis Of System Identification Techniques For Nonlinear Modeling Of The Neuron-Microelectrode Junction

Keywords

Microelectrode array; NARX model; Neural network; Neuroelectronic interface; Neuron-electrode junction; Wiener model

Abstract

Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction. Copyright © 2013 American Scientific Publishers. All rights reserved.

Publication Date

3-1-2013

Publication Title

Journal of Computational and Theoretical Nanoscience

Volume

10

Issue

3

Number of Pages

573-580

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1166/jctn.2013.2736

Socpus ID

84876543515 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84876543515

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