motorunitforce_matching

This package contains MATLAB code that generates motor unit (MU) pools and uses them to perform force matching tasks used in

Murtola & Richards (2024) Matching dynamically varying forces with multi-motor-unit muscle models: A simulation study. BioRxiv. https://doi.org/10.1101/2024.02.13.580042

Preliminaries

Requirements: - Matlab (2023a), compatibility with other recent versions is likely but not guaranteed - Parameter optimisation also requires Matlab toolboxes: Global optimisation toolbox (4.8.1) and (optionally) Parallel computing toolbox (7.8)

Package contents: - runforcematching.mlx - main MATLAB script for generating MU pools and running force matching tasks - optimiseneuraldriveparameters.mlx - MATLAB script to optimise and save neural drive parameters - plotfiringratefigures.mlx - MATLAB script to simulate trapezoidal tasks and plot firing rate figures - helper functions and class definitions: - MUpool - class definition for MUpool objects - muscleMUsettings - function setting MU pool parameters - generateMUpool - function generating a set of MU pools from parameters - forcematchingtasks - function generating force and FVL profiles - reachingdata.mat - data for simulated reaching tasks - optimparamsTAprecomp.mat & optimparamsSFprecomp.mat - precomputed set of neural drive parameters

Usage

Make sure all functions and data files are on Matlab's search path.

To use runforcematching.mlx: 1. Use commands on lines 1-6 to select muscles and corresponding parameter files. 2. (optional) To modify MU pool parameters, open muscleMUsettings.m and edit values on lines 8-41. 3. Run the script.

To use optimiseneuraldrive_parameters.mlx: 1. Set parameters for the set of elementary tasks to be used for optimisation. taskDur = duration of muscle length change (e.g. 2 sec), initLength = the initial normalised muscle length (e.g. 1 for task to start at optimal length, l0) lengthChange = relative length change for each task (e.g. [-.2 0 2] for three tasks: concentric to 0.8l0, isometric, and eccentric to 1.2l0) 2. (optional) Change muscle-specific force scaling for tasks on lines 15/17, if desired. 3. Select muscle on line 7. (optimise one muscle at a time). 4. (optional) The optimisation uses precomputed parameter values as a partial initial population. To change the file used, edit line 35. To optimise without initial population, comment out lines 35-36 and 49, and remove the variable initVals from the function call on line 55. 5. Set filename for saving results on line 62. 6. Run the script.

To use plotfiringratefigures.mlx: 1. Use first two sections similarly to runforce_matching.mlx. 2. For the third section, download reference data as instructed and specify its loaction. 3. Run the script.