This code is an implementation of an optimal control theory model of the rodent compensatory eye movement system and is fully described in the paper.A neuroanatomically grounded optimal control model of the compensatory eye movement system. Holland et al. Within this folder there are 2 slighlty different approaches: 1- basic model functionality: This folder contains the functions required to run the model and two scripts that use the functions as basic examples. createSimDataSPFC.m - this script runs the model through the 4 CEM conditions (VOR,OKR,VVOR,SVOR) createAdaptDataSPFC.m - this script runs the model through a VOR gain down adaptation paradigm (with and without a flocculus lesion) Neither of these scripts require any saved data files to run. 2- Comparison of Model To Data makeFiguresSPFC.m - this script reproduces all figures in the paper, it loads the bayesian estimates, sum of sines stimuli and, adaptation data. These files are included in the download but should not be renamed unless accompanied by edits to this script. All filenames are defined as variables towards the start of the script. sosStimuli.mat - the field stimuliForModel.stimVel contains the stimuli used for the experimental data in Sibindi et al 2016 IOVS. These are extended and applied to the model gainDownAdaptationData.mat - the gain down data is in a matrix which is numMice (7) x testSession (6) each value is the gain for a mouse at that test trial (VOR trial). The training sessions occur between each test trial BayesFittingSPFC-10kSamples-final.mat - the final output of the script in the 'Data Analysis' folder, contains the final bayesian estimates of gain and phase in eyeResults.stats and the full sampe history in eyeResults.samples. Examples of the figure ouputs are contained in the subfolder 'paperFiguresSPFC' and explanations can be found in the text Another useful feature may be the exFigFlag (line 31), if set to 0 this will not export the figures to pdf via export_fig (which must have another program installed to export to .pdf or .png), instead the figures are kept open in matlab. This removes the need to install the associated programs for export_fig although a lot of figures (>100) are porduced on a full run of all sections and this can crash matlab. Help documentation for each of the functions is provided within them, any bugs or questions please contact Peter Holland p.holland@erasmusmc.nl. Changelog: %all files tagged with v1.0 on upload 02/09/2016 We make use of the 'panel' File Exchange submission and are thankful for contribution. SubAxis SplitVec naninterp export_fig matbugs Waitbar nanconv This code has been tested on Matlab versions 2013a and 2015a on windows, very old versions of matlab (pre inputParser) will not be compatible.