Introduction

This repository contains models and tools discussed in the following paper:

Fietkiewicz, C., McDougal, Robert A., Corrales Marco, D., Chiel, H. J. and Thomas, P. J. (2023). Tutorial: using NEURON for neuromechanical simulations. Frontiers in Computational Neuroscience 17: 1143323. [Link]

All models require NEURON to be installed, and Python versions require Python.

Address questions and comments to Dr. Chris Fietkiewicz (fietkiewicz@hws.edu). The following sections are available:

Models

This collection of NEURON models demonstrates various concepts for neuromechanical simulations using pointers, as detailed in the paper above (Fietkiewicz et al.).

Each model is independent of the others, with all necessary files in a single directory. Most models have both a hoc version and a Python version, each in a separate directory. The following steps can be used to run each of the models and produce the output shown in the paper cited above:

1. Compile the .mod files in the selected directory.

2. For most models, run either run.hoc or run.py, depending on the version you are working with. For “5-AplysiaLoop”, the hoc version has two different .hoc files, each of which uses a different setting for the parameter “mu” (see the paper for details).

3. For hoc versions, click Init & Run from the "Run Control" GUI. Most Python versions run the simulation automatically. For “5-AplysiaLoop”, however, the Python version provides a single GUI where certain parameters may be set and the user must click the Init & Run button.

The following models are available:

1-Neuromuscular

Neuromuscular is a neuromuscular model of calcium dynamics and muscle force. It demonstrates basic usage of pointers with an example of calcium dynamics and muscle force. It is adapted from the following:

• Kim, Hojeong. "Muscle length-dependent contribution of motoneuron Cav1. 3 channels to force production in model slow motor unit." Journal of Applied Physiology 123.1 (2017): 88-105.

• Kim, Hojeong. "Linking motoneuron PIC location to motor function in closed-loop motor unit system including afferent feedback: a computational investigation." Eneuro 7.2 (2020).

• Kim, Hojeong, and Charles J. Heckman. "A dynamic calcium-force relationship model for sag behavior in fast skeletal muscle." PLOS Computational Biology 19.6 (2023): e1011178.

2-OscillatorLoop

OscillatorLoop is a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system. It contrasts the differences between state variables and parameters. It is adapted from the following:

• Yu, Zhuojun, and Peter J. Thomas. "Dynamical consequences of sensory feedback in a half-center oscillator coupled to a simple motor system." Biological Cbernetics 115.2 (2021): 135-160.

3-RespirationLoop

RespirationLoop uses the NEURON implicit management of ionic currents with an application for respiratory control. It is adapted from the following:

• Diekman, Casey O., Peter J. Thomas, and Christopher G. Wilson. "Eupnea, tachypnea, and autoresuscitation in a closed-loop respiratory control model." Journal of Neurophysiology 118.4 (2017): 2194-2215.

4-NonsmoothOscillator

NonsmoothOscillator is a small, pedagogical model that demonstrates basic techniques for incorporating nonsmooth dynamics.

5-AplysiaLoop

AplysiaLoop is a closed-loop model of feeding behavior in the sea hare Aplysia californica. It uses a firing rate model for control of musculature and incorporates a more advanced example of nonsmooth dynamics. It is adapted from the following:

• Shaw, Kendrick M., David N. Lyttle, Jeffrey P. Gill, Miranda J. Cullins, Jeffrey M. McManus, Hui Lu, Peter J. Thomas, and Hillel J. Chiel. "The significance of dynamical architecture for adaptive responses to mechanical loads during rhythmic behavior." Journal of Computational Neuroscience 38 (2015): 25-51.

• Lyttle, David N., Jeffrey P. Gill, Kendrick M. Shaw, Peter J. Thomas, and Hillel J. Chiel. "Robustness, flexibility, and sensitivity in a multifunctional motor control model." Biological Cybernetics 111 (2017): 25-47.

• Wang, Y., Gill, J. P., Chiel, H. J., & Thomas, P. J. Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems. Biological Cybernetics, (2022): 1-24.

6-LotkaVolterra

LotkaVolterra is based on the classic Lotka-Volterra two-population predator-prey model (https://en.wikipedia.org/wiki/Lotka-Volterra_equations). It is a small, 2-dimensional system that demonstrates basic pointer usage.

PointerBuilder apps

These applications are graphical interfaces for working with NEURON pointers. They can be used to learn and verify pointer syntax.

• NEURON: This graphical interface, written entirely in NEURON, creates pointer instructions in hoc syntax.

• Python: This graphical interface, written entirely in Python, creates pointer instructions in Python syntax. It requires the Tkinter package, which is common to most Python installations.