Efficient micro-electrode recording modeling using a filtered point process

Weegink, Kristian J., Bellette, Paul A., Varghese, John J., Silburn, Peter A., Meehan, Paul A. and Bradley, Andrew P. (2013) Efficient micro-electrode recording modeling using a filtered point process. arXiv Quantitative Biology: Neurons and Cognition, 1307 5250: 1-22.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ324841_OA.pdf Full text (open access) application/pdf 1.11MB 26
Author Weegink, Kristian J.
Bellette, Paul A.
Varghese, John J.
Silburn, Peter A.
Meehan, Paul A.
Bradley, Andrew P.
Title Efficient micro-electrode recording modeling using a filtered point process
Journal name arXiv Quantitative Biology: Neurons and Cognition
Publication date 2013-07-19
Sub-type Article (original research)
Open Access Status File (Publisher version)
Volume 1307
Issue 5250
Start page 1
End page 22
Total pages 22
Place of publication Davis, CA, USA
Publisher Cornell University Library
Language eng
Formatted abstract
In this paper we present an efficient model of the neuronal potentials recorded by a deep brain stimulation microelectrode (DBS MER) in the subthalamic nucleus. It is shown that a computationally efficient filtered point process consisting of 10,000 neurons, including extracellular filtering closely matches recordings from 13 Parkinson's disease patients. The recordings were compared using their voltage amplitude distributions, power spectral density estimates and phase synchrony. It was found that interspike interval times modeled using a Weibull distribution with a shape parameter of 0.8, slightly non-Poisosnian, gave the best fit of the simulations to patient recordings. These results indicate that part of the `background activity' present in an DBS MER can be considered to be a very local field potential due to the surrounding neuronal activity.Therefore, the statistics of the interspike interval times modify the structure of the background activity.
Q-Index Code CX
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mechanical & Mining Engineering Publications
 
Versions
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Created: Wed, 05 Mar 2014, 12:53:38 EST by Alex Fitzgerald on behalf of School of Mechanical and Mining Engineering