Simulations of cochlear nucleus bushy cells reconstructed from serial blockface electron microscopy (V1.3)

Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory- nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically-based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically-detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes. 

Methods
1. Description of methods used for collection/generation of data: Reconstructions were made in syGlass (IstoVisio, Morgantown, WV) as SWC files (Cannon et al., 1988), and converted to the HOC format for use in NEURON for simulations. The SWC files were traced by hand from semi-automated mesh and annotated reconstructions of the cell membranes (soma, dendrites, axons).

2. Methods for processing the data: The SWC files were converted to the HOC format for use in NEURON for simulations. The repository of the code for running the simulations is at https://github.com/pbmanis/VCNModel (release version 1.0.0), and is based on the cnmodel package (https://github.com/cnmodel/cnmodel; Manis and Campagnola, 2018). 

3. Instrument- or software-specific information needed to interpret the data: Python 3.8 or later. The complete set of package requirements and a script to build the required environment can be found in requirements.txt at the github repository listed in (2)