About this Journal Submit a Manuscript Table of Contents
Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 575129, 8 pages
http://dx.doi.org/10.1155/2012/575129
Research Article

Analyzing the Effects of Gap Junction Blockade on Neural Synchrony via a Motoneuron Network Computational Model

1Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4440, USA
2Department of Physiology & Biophysics, Stony Brook University, Stony Brook, NY 11794-8661, USA
3Program in Neuroscience, SUNY, Stony Brook University, Stony Brook, NY 11794-5230, USA

Received 20 April 2012; Revised 11 October 2012; Accepted 22 October 2012

Academic Editor: Karim Oweiss

Copyright © 2012 Heraldo Memelli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In specific regions of the central nervous system (CNS), gap junctions have been shown to participate in neuronal synchrony. Amongst the CNS regions identified, some populations of brainstem motoneurons are known to be coupled by gap junctions. The application of various gap junction blockers to these motoneuron populations, however, has led to mixed results regarding their synchronous firing behavior, with some studies reporting a decrease in synchrony while others surprisingly find an increase in synchrony. To address this discrepancy, we employ a neuronal network model of Hodgkin-Huxley-style motoneurons connected by gap junctions. Using this model, we implement a series of simulations and rigorously analyze their outcome, including the calculation of a measure of neuronal synchrony. Our simulations demonstrate that under specific conditions, uncoupling of gap junctions is capable of producing either a decrease or an increase in neuronal synchrony. Subsequently, these simulations provide mechanistic insight into these different outcomes.