Glial cells, previously thought to have generally supporting roles in the central nervous system, are emerging as essential contributors to multiple aspects of neuronal circuit function and development. This review focuses on the contributions of glial cells to the development of auditory pathways in the brainstem. These pathways display specialized synapses and an unusually high degree of precision in circuitry that enables sound source localization. The development of these pathways thus requires highly coordinated molecular and cellular mechanisms. Several classes of glial cells, including astrocytes, oligodendrocytes and microglia, have now been explored in these circuits in both avian and mammalian brainstems. Distinct populations of astrocytes are found over the course of auditory brainstem maturation. Early appearing astrocytes are associated with spatial compartments in the avian auditory brainstem. Factors from late appearing astrocytes promote synaptogenesis and dendritic maturation, and astrocytes remain integral parts of specialized auditory synapses. Oligodendrocytes play a unique role in both birds and mammals in highly regulated myelination essential for proper timing to decipher interaural cues. Microglia arise early in brainstem development and may contribute to maturation of auditory pathways. Together these studies demonstrate the importance of non-neuronal cells in the assembly of specialized auditory brainstem circuits.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.