Comparative Analysis of System Identification Techniques for Nonlinear Modeling of the Neuron-Microelectrode Junction

Saad Ahmad Khan, Vaibhav Thakore, Aman Behal*, Ladislau Boeloeni, James J. Hickman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Pages (from-to)573-580
Number of pages8
JournalJournal of Computational and Theoretical Nanoscience
Volume10
Issue number3
DOIs
Publication statusPublished - Mar 2013
MoE publication typeA1 Journal article-refereed

Keywords

  • Microelectrode Array
  • Wiener Model
  • NARX Model
  • Neural Network
  • Neuroelectronic Interface
  • Neuron-Electrode Junction
  • STIMULATION
  • RECORDINGS
  • TRANSISTOR
  • NETWORKS
  • CELLS

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