Using a computational model to analyze the effects of firing frequency on synchrony of a network of gap junction-coupled hypoglossal motoneurons.

Hypoglossal motoneurons (HMs) are located in the brainstem and play an important role in the maintenance of upper airway patency. HMs are known to be coupled to one another via gap junctions and exhibit synchronous firing behavior when driven by premotor inputs. In the current study, we used a computational model to analyze the influence of firing frequency on synchronous firing behavior of a network of gap junction-coupled HMs. As there are many factors that can influence excitability and firing frequency of HMs, our simulations were focused on the effects of modulation of SK channel conductance and the magnitude of the input currents as a means of modifying firing frequency. Our simulations revealed that regardless of the mechanism by which firing frequency was modulated, increasing the firing frequency disrupted the synchrony in gap junction-coupled HM networks with low, medium, and high levels of gap junction coupling.

Application of a "staggered walk" algorithm for generating large-scale morphological neuronal networks.

Large-scale models of neuronal structures are needed to explore emergent properties of mammalian brains. Because these models have trillions of synapses, a major problem in their creation is synapse placement. Here we present a novel method for exploiting consistent fiber orientation in a neural tissue to perform a highly efficient modified plane-sweep algorithm, which identifies all regions of 3D overlaps between dendritic and axonal projection fields. The first step in placing synapses in physiological models is neurite-overlap detection, at large scales a computationally intensive task. We have developed an efficient "Staggered Walk" algorithm that can find all 3D overlaps of neurites where trillions of synapses connect billions of neurons.

Analyzing the effects of gap junction blockade on neural synchrony via a motoneuron network computational model.

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.