CNModel is a Python-based interface to NEURON models of cochlear nucleus neurons, synapses, network connectivity. To drive the model with sound stimuli, the Zilaney et al (2010, 2014) auditory periphery model is used to generate auditory nerve spike trains (either via the "cochlea" Python package or by the original MATLAB model; see below). The overall goal is to provide a platform for modeling networks of cochlear nucleus neurons with different levels of biological realism in the context of an accurate simulation of auditory nerve input.

At the lowest level are NEURON-based implementations of ion channels and synapses. Ion channel models for potassium channels are derived largely from the measurements and models of Rothman and Manis (2003abc), and Kanold and Manis (1999, 2001); other channels are derived or modified from the literature. Cell models are in turn based on the insertion of ion channels in densities based on measurements in guinea pig and mouse. The "point" somatic cell models, which form the base set of models in CNModel, replicate the data reported in the original papers.

The postsynaptic receptor/conductance models are based on kinetic models of glutamate (Raman and Trussell, 1992) and glycinergic receptors, as adjusted to match measurements of synaptic conductances from the mouse cochlear nucleus collected in Xie and Manis, 2013. The glutamate receptor models include desensitization and the effects of internal polyamine receptor block, based on the kinetic scheme of Woodhull (1982).

The presynaptic release model includes a multisite, probabilistic synapse that includes time-dependent changes in release probability based on the Dittman, Kreitzer and Regehr (J Neurosci. 2000 Feb 15;20(4):1374-85) kinetic scheme. Although detailed, this model is computationally expensive and likely not suitable for large scale network simulations. Other simpler models of synapses are also included.

Network connectivity may be defined programmatically, or based on a table of connectivity patterns. A table with estimates derived from the literature is included in the source.

Included in this package is a set of test suites for different components. An overriding set of unit tests is available to confirm performance of the base models against a set of reference runs, several of which are in turn directly traceable to the original manuscripts. The test suites are useful in verifying performance of the model after modifications of the code or changes in the installation (upgrades of Python or Matlab, for example).

This package depends on the following:

1. Python 2.7.10 with numpy (1.11), scipy (0.19.0), lmfit (0.9.6), pyqt4 (4.11.4), matplotlib (2.0 or 1.5) and pyqtgraph (0.9.10). An Anaconda install with the appropriate scientific packages works well. lmfit is best obtained via pip to install the latest versions. This package is not yet compatible with Python 3.x.
2. A Python-linked version of NEURON (www.neuron.yale.edu). The code has been tested with NEURON 7.3 and 7.4.
3. A C compiler (gcc). Needed for compilation of mechanisms for NEURON.
4. The Zilany et al (JASA 2014) auditory periphery model. This can be provided one of two ways:
   • The original MATLAB-based Zilany model; requires MATLAB 2011 or later. A C compiler will also be needed to build this model.The model should be placed in the directory "cnmodel/cnmodel/an_model/model", with the following components: ANmodel.m, complex.c, complex.h, complex.hpp, ffGn.m, fituaudiogram2.m, mexANmodel.m, model_IHC.c, model_Synapse.c, and the THRESHOLD_ALL_* files.
   • The Python-based cochlea model by Rudnicki and Hemmert (https://github.com/mrkrd/cochlea; can be installed via pip).
5. neuronvis (optional; available at https://github.com/campagnola/neuronvis or https://github.com/pbmanis/neuronvis). This provides 3D visualization for morphology.

After the code is installed, enter the cnmodel directory and compile the NEURON mod files:

$ nrnivmodl cnmodel/mechanisms

This will create a directory ("x86_64" or "special") in the top cnmodel directory with the compiled mechanisms.

At that point

$ python examples/toy_model.py

should generate a plot with several sets of traces showing responses of individual neuron models to depolarizing and hyperpolarizing current steps.

The test suite should be run as:

$ python test.py

This will test each of the models against reference data, the synapse mechanisms, a number of internal routines, and the auditory nerve model. The tests should pass for each component. Failures may indicate incorrect installation or incorrect function within individual components. These should be corrected before proceeding with simulations.

The data for the figures in the manuscript (Manis and Campagnola, Hearing Research, submitted) can be generated using the bash script "figures.sh" in the examples subdirectory. From the main cnmodel directory: $ ./examples figures.sh fignum

where fignum is one of 2a, 2b, 2c, 3, 4, 5, 6a, 6b, or 7.

Note that Figure 7 may take several hours to generate.

A number of additional tests are included in the examples directory.

• test_an_model.py verifies that the auditory nerve model can be run. If necessary, it will compile (using MEX) the mechanisms for matlab.

• test_ccstim.py tests the generation of different stimulus waveforms by the pulse generator module.

• test_cells.py runs different cell models in current or voltage clamp.

  • usage: test_cells.py celltype species[-h] [--type TYPE] [--temp TEMP] [-m MORPHOLOGY] [--nav NAV] [--ttx] [-p PULSETYPE] [--vc | --cc | --rmp] For example: python test_cells.py bushy mouse --cc --temp 34

• test_cells.py can run protocols on selected cell models. -usage: test_cells.py [-h] [--type TYPE] [--temp TEMP] [-m MORPHOLOGY] [--nav NAV] [--ttx] [-p PULSETYPE] [--vc | --cc | --rmp] celltype species

• test_circuit.py tests the generation of circuits with populations of cells. No simulations are run.

• test_decorator.py generates an IV curve for the reconstructed cell LC_bushy.hoc (Figure 5B,C)

• test_mechanisms.py runs a voltage clamp I/V protocol on a selected mechanism and displays the result.

  • Usage: python test_mechanisms.py

    Available channel mechanisms:

  Channel	Channel	Channel	Channel	Channel
  CaPCalyx	KIR	bkpkj	hcno	hcnobo
  hh	hpkj	ihpyr	ihsgcApical	ihsgcBasalMiddle
  ihvcn	jsrna	k_ion	ka	kcnq
  kdpyr	kht	kif	kis	klt
  kpkj	kpkj2	kpkjslow	kpksk	leak
  lkpkj	na	naRsg	na_ion	nacn
  nacncoop	nap	napyr	nav11

• test_mso_inputs.py runs a circuit that creates a point MSO neuron, innervated by bushy cells from independent "ears". This demonstrates how to construct a binaural circuit using CNModel.

• test_physiology.py runs a large VCN circuit that converges onto a single bushy cell. This run can take a long time. The output was used to create Figure 7 of the manuscript.

• test_populations.py tests synaptic connections between two cell types. Usage: python test_populations.py <pre_celltype> <post_celltype>

• test_sgc_input_phaselocking.py tests phase locking with SGC inputs to a bushy cell.

• test_sgc_input_PSTH.py shows SGC inputs and postsynaptic bushy cell PSTHs.

• test_sgc_input.py demonstrates SGC input to a VCN bushy cell.

• test_simple_synapses.py tests simple Exp2Syn inputs to different cell types.

  • Usage: python test_synapses.py <pre_celltype> <post_celltype> Supported cell types: sgc, bushy, tstellate, dstellate, tuberculoventral, pyramidal

• test_sound_stim.py generates spike trains from the selected model (cochlea, matlab) and plots rate-intensity functions for the 3 different SR groups.

• test_sounds.py generates waveforms for different kinds of sounds included in the sounds class.

• test_synapses.py evokes spikes in a presynaptic cell while recording the postsynaptic potential.

  • Usage: python test_synapses.py <pre_celltype> <post_celltype> Supported cell types: sgc, bushy, tstellate, dstellate

• toy_model.py generates IV plots for each of the principal point cell types included in CNModel. This is the data for Figure 3 of the manuscript.

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12. Rothman JS, Manis PB. Differential expression of three distinct potassium currents in the ventral cochlear nucleus. J Neurophysiol. 2003 Jun;89(6):3070-82. PubMed PMID: 12783951.

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