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Model predictive control model for an isometric motor task (Ueyama 2017)
Home Page: https://modeldb.yale.edu/185338
This project forked from modeldbrepository/185338
Model predictive control model for an isometric motor task (Ueyama 2017)
Home Page: https://modeldb.yale.edu/185338
<html><pre> This is the readme for the models associated with the paper: Ueyama Y (2017) Optimal feedback control to describe multiple representations of primary motor cortex neurons. J Comput Neurosci 43:93-106 DOI: <a href="http://doi.org/10.1007/s10827-017-0650-z">10.1007/s10827-017-0650-z</a> Y Ueyama supplied the matlab code. It requires <a href="http://people.ee.ethz.ch/~mpt/3/">mpt</a> to be installed. As mentioned in the paper "The downloaded files are composed of one data set, ‘data.mat,’ and three codes: ‘DesignMPC.m,’ ‘RunSimulation.m,’ and ‘PlotSensoryGain.m.’ DesginMPC.m designs the model predictive controller for the isometric torque production task, and replicates the dataset data.mat. RunSimulator.m loads data.mat, executes the simulation, and produces some of the figures found in Figs. 3-4 and 9. PlotSensoryGain.m also carries out the simulation and plots the figures found in Figs. 5 and 6." If you have mpt installed (see above link) typing RunSimulation on the matlab command line will generate the following figures similar to the paper's figure 3a: <img src="./screenshot1.png" alt="screenshot 1"> figure 2b: <img src="./screenshot2.png" alt="screenshot 2"> figure 4a: <img src="./screenshot3.png" alt="screenshot 3"> figure 9b top, 9c top: <img src="./screenshot4.png" alt="screenshot 4"> </pre></html>
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