GitHub - ModelDBRepository/261878: Decoding movement trajectory from simulated grid cell population activity (Bush & Burgess 2019)

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commits
humanGridSim.m		humanGridSim.m
readme.html		readme.html
sampleEEG.mat		sampleEEG.mat
screenshot1.png		screenshot1.png
screenshot2.png		screenshot2.png
screenshot3.png		screenshot3.png
trackingData.mat		trackingData.mat

Repository files navigation

<html>
<p>This is the readme for the simulation code associated with the paper:
</p><p>Bush D, Burgess N (2020) Detection and Advantages of Phase Coding in 
the Absence of Rhythmicity. Hippocampus
</p><p>This Matlab code was contributed by Dr Daniel Bush.
</p>
Usage
<p>Note this software also requires the fastsmooth function available from matlab file exchange:
  <a href="https://www.mathworks.com/matlabcentral/fileexchange/19998-fast-smoothing-function">
    https://www.mathworks.com/matlabcentral/fileexchange/19998-fast-smoothing-function</a><br/>

Download and extract this archive, add the functions from the above link to matlab's path (or add
the functions to the extracted folder) and cd to the extracted folder. 
Set your simulation preferences (see the annotations at the top of 
the 'humanGridSim' script), or use the defaults by simply creating
an empty 'in' structure (can type on matlab command line
in=cell2struct(cell(1,1),{'gridScales'});), and then execute:
</p><p>[in,pos,LFP,gridCells,cycle,cycleDecoding] = humanGridSim(in);
</p><p>As an example, to simulate grid cell firing patterns in a 1D 
environment and decode the movement trajectory from grid cell activity
in each oscillatory cycle, execute:
</p><p>in.environment = '1D';<br/>
[in,pos,LFP,gridCells,cycle,cycleDecoding] = humanGridSim(in);
</p><p>This should take &lt;30 seconds to run and then produce the figure below
(cf. Figures 5 and 6 in Bush &amp; Burgess, 2020):
</p><p><img src="./screenshot1.png" alt="screenshot 1" width="650">
</p><p>Alternatively, to simulate grid cell firing patterns in a 2D 
environment and then decode the movement trajectory from grid cell 
activity in each oscillatory cycle, execute:
</p><p>in.environment = '2D';<br/>
[in,pos,LFP,gridCells,cycle,cycleDecoding] = humanGridSim(in);
</p><p>This should take ~30 minutes to run and then produce the figure below
(cf. Figures 5 and 6 in Bush &amp; Burgess, 2020):
</p><p><img src="./screenshot2.png" alt="screenshot 2" width="650">
</p><p>Further inputs can be provided as fields of the 'in' structure (see
annotations at the top of the 'humanGridSim' script for details). 
For example, the ability to decode movement direction from grid cell 
activity in each oscillatory cycle can be eliminated by simulating
'phase locked' grid firing. This can be achieved by executing:
</p><p>in.environment = '2D';<br/>
in.phaseLock = 'true';<br/>
[in,pos,LFP,gridCells,cycle,cycleDecoding] = humanGridSim(in);
</p><p>This should take ~30 minutes to run and then produce the figure below
(cf. Figures 5 and 6 in Bush &amp; Burgess, 2020):
</p><p><img src="./screenshot3.png" alt="screenshot 3" width="650">
</p><p>Comments, criticisms and questions should be directed to
drdanielbush at gmail.com
</p>
</html>