https://github.com/gkaguirre/msutemporalintegration

Demo implementation of the Mattar 2016 exponential integration model using the forwardModel platform
https://github.com/gkaguirre/msutemporalintegration

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Demo implementation of the Mattar 2016 exponential integration model using the forwardModel platform

• Host: GitHub
• URL: https://github.com/gkaguirre/msutemporalintegration
• Owner: gkaguirre
• Created: 2024-01-24T22:52:49.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-04-10T16:14:09.000Z (9 months ago)
• Last Synced: 2024-04-10T19:59:13.286Z (9 months ago)
• Language: MATLAB
• Size: 56.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# msuTemporalIntegration

Demo implementation of the [Mattar 2016](https://www.cell.com/current-biology/pdf/S0960-9822(16)30419-5.pdf) exponential integration model using the [forwardModel](https://github.com/gkaguirrelab/forwardModel) non-linear fitting platform

The code can be configured using the Matlab package manager [Toolbox Toolbox](https://github.com/ToolboxHub/ToolboxToolbox). Alternatively, the following repositories should be downloaded and placed on the Matlab path:

- https://github.com/gkaguirrelab/forwardModel

- https://github.com/freesurfer/freesurfer

There are hard-coded paths to the data and event files in the routine `demoMattarAdapt`. There are also a couple of parameters there that one could adjust (e.g., spatial smoothing).

Once you have the paths updated, run the demo. The demo script has the flag `fitOneVoxel` available at the start. Set this to `true`, and the routine will quickly (in seconds) fit the data for an example voxel. You can use this to confirm that your paths are working properly. Then, set `fitOneVoxel` to false and run the demo again. The routine will fit the model to all voxels. This takes about 40 minutes on a laptop with 10 CPU cores available for the parpool.

The results of the analysis will be written to the hard-coded save directory. The output includes a set of files in .nii format that provide maps of the relevant parameter values.