Contact: Insub Kim (insubkim@stanford.edu)
Scripts were developed using MATLAB 2020b (Mac OS/Linux).
Toolboxes: SpatiotemporalpRFs, PRFmodels, Vistasoft, BADS
You can download the all the toolboxes by running:
% MATLAB: download_toolboxes.m
download_toolboxes('toolbox_urls.txt', 'code/toolbox')It is recommended to run it on a machine with a GPU to solve the spatiotemporal pRF estimates
Click to view it on YouTube
%% spatiotemporal pRF Demo (run.m)
% - download toolboxes
% - set variables and create JSON files
% - create synthetic timecourses
% - solve spatiotemporal PRF models
% - plot results
run.m
The software takes stimulus information and a JSON file (example JSONfile) as input and generates synthetic timecourses with noise for the three different pRF models: spatial, DN-ST, and CST.
JSON file: To compute spatiotemporal pRF models, the JSON file's temporal class must be specified. Three different types of pRF models can be used: spatial pRF model, CST model, and DN-ST model. The spatial pRF model is similar to the one-Gaussian pRF model but uses a higher temporal sampling resolution. CST and DN-ST models are spatiotemporal pRF models that have explicit temporal impulse response functions.
For more detailed information about how to edit the JSON file (Link)
Solve the parameters for each model(spatial, DN-ST, and CST). Synthetic timecourses generated for each model are solved by the same model.
Compare the ground truth and predicted timecourses for each model, and plot the results.
- Code to regenerate figures in the paper Link
Dumoulin, S. O., & Wandell, B. A. (2008). Population receptive field estimates in human visual cortex. Neuroimage, 39(2), 647-660.
Lerma-Usabiaga, G., Benson, N., Winawer, J., & Wandell, B. A. (2020). A validation framework for neuroimaging software: The case of population receptive fields. PLoS computational biology, 16(6), e1007924.
Zhou, J., Benson, N. C., Kay, K., & Winawer, J. (2019). Predicting neuronal dynamics with a delayed gain control model. PLoS computational biology, 15(11), e1007484.
Stigliani, A., Jeska, B., & Grill-Spector, K. (2019). Differential sustained and transient temporal processing across visual streams. PLoS computational biology, 15(5), e1007011.