Although spontaneous blinking is one of the most frequent human movements, little is known about its neural basis. We developed a rat model of spontaneous blinking in order to identify and better characterize the spontaneous blink generator. We monitored spontaneous blinking for 55 min periods in normal conditions and after the induction of mild dry eye or dopaminergic drug challenges. The normal spontaneous blink rate was 5.3 ± 0.3 blinks/min. Dry eye or 1 mg/kg apomorphine significantly increased and 0.1 mg/kg haloperidol significantly decreased the blink rate. Additional analyses revealed a consistent temporal organization to spontaneous blinking with a median 750 s period that was independent of the spontaneous blink rate. Dry eye and dopaminergic challenges significantly modified the regularity of the normal pattern of episodes of frequent blinking interspersed with intervals having few blinks. Dry eye and apomorphine enhanced the regularity of this pattern, whereas haloperidol reduced its regularity. The simplest explanation for our data is that the spinal trigeminal complex is a critical element in the generation of spontaneous blinks; incorporating reflex blinks from dry eye and indirect basal ganglia inputs into the blink generator. Although human subjects exhibited a higher average blink rate (17.6 ± 2.4) than rats, the temporal pattern of spontaneous blinking was qualitatively similar for both species. These data demonstrate that rats are an appropriate model for investigating the neural basis of human spontaneous blinking and suggest that the spinal trigeminal complex is a major element in the spontaneous blink generator.

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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.

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.

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