DOI: 10.5061/dryad.4j0zpc8g1
title: Simulations of cochlear nucleus bushy cells reconstructed from serial blockface electron microscopy (V1.3)

description: 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. 
description: 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). <br><br>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)
description: The programs that generated these files and that generate plots
 for the figures in the paper are at the github repository listed in
 Methods. The simulation data and a reference table is
 provided as files and compressed archives (zip files), which will need to
 be unzipped into the appropriate target directories. The files should be
 placed in a folder (named something like
 "BU_simulation_data"). The compressed archives named
 "Impedance Calculations" and "VCN_nn" should placed in
 a subdirectory called "Simulations", and uncompressed there. The
 compressed file "IntermediateAnalyses" should be placed under
 the top directory (e.g., "BU_simulation_data"), and uncompressed
 there. Refer to the instructions in the github archive VCNModel. Paths to
 data will need to be edited in the file
 "wheres_my_data.toml". The uncompressed data
 set is approximately 180 GB.