Tutorial: using NEURON for neuromechanical simulations

Chris Fietkiewicz; Robert A McDougal; David Corrales Marco; Hillel J Chiel; Peter J Thomas

doi:10.3389/fncom.2023.1143323

Tutorial: using NEURON for neuromechanical simulations

Front Comput Neurosci. 2023 Jul 31:17:1143323. doi: 10.3389/fncom.2023.1143323. eCollection 2023.

Authors

Chris Fietkiewicz¹, Robert A McDougal^{2

3

4

5}, David Corrales Marco¹, Hillel J Chiel^{6

7

8}, Peter J Thomas^{6

9

10

11

12}

Affiliations

¹ Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States.
² Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.
³ Wu Tsai Institute, Yale University, New Haven, CT, United States.
⁴ Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.
⁵ Section for Biomedical Informatics, Yale School of Medicine, New Haven, CT, United States.
⁶ Department of Biology, Case Western Reserve University, Cleveland, OH, United States.
⁷ Department of Neurosciences, Case Western Reserve University, Cleveland, OH, United States.
⁸ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
⁹ Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States.
¹⁰ Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, United States.
¹¹ Department of Electrical, Control, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States.
¹² Department of Data and Computer Science, Case Western Reserve University, Cleveland, OH, United States.

Abstract

The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a "periphery" and outputs to that periphery. In other words, "body" dynamics are driven in part by "brain" variables, such as voltages or firing rates, and "brain" dynamics are influenced by "body" variables through sensory feedback. To couple the "brain" and "body" components, we use NEURON's pointer construct to share information between "brain" and "body" modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth "brain/body" system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models.

Code available at: https://github.com/fietkiewicz/PointerBuilder.

Keywords: biomechanics; body; brain; closed-loop; motor control; neural network.

Grants and funding

This work was supported in part by National Institutes of Health BRAIN Initiative Grant RF1 NS118606-01 and the Oberlin College Department of Mathematics.