Synchronization of hypoglossal motoneurons

Synchronous activity of neurons arises in contexts as diverse as motor function and cognition as well as pathologies such as epileptic seizures and muscle tremors. Yet the actual mechanisms of synchronization are poorly understood. Most studies have focused on the neurons themselves while using extremely simplistic models of inputs to the neurons. However the properties of the inputs are often as unique as the neurons themselves and are equally important in determining the level of synchrony seen at the outputs. We used a combined experimental and modeling approach to study the effects of endogenous inputs on the synchronization of hypoglossal motoneurons. Using paired intracellular recordings, we show that synchrony is primarily due to the correlation of low-frequency waves of excitatory synaptic inputs. This synchrony is modulated by input variability and can exhibit phase differences as large as 100 milliseconds. Computational modeling with synthetic inputs suggests that output synchrony has a linear relationship with low-frequency input correlation. A network model consisting of multiple neuronal clusters successfully demonstrates the phase relationship observed experimentally.