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Shared Response Model (SRM) of NIPS 2015

SRM 최신버젼 다운로드

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# Shared Response Model (SRM)

Library for Shared Response Model, related methods and experiment pipelines

Developed by Cameron PH Chen @ Princeton (https://cameronphchen.github.io)

##### If you only want to use SRM on your dataset, checkout [Use_SRM](https://github.com/cameronphchen/Use_SRM)

### Paper:

If you use this code or SRM in scientific publication, citing the following paper is appreciated:

**A Reduced-Dimension fMRI Shared Response Model**

Po-Hsuan Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James V. Haxby, Peter J. Ramadge 
Advances in Neural Information Processing Systems (NIPS), 2015. 
[Paper](http://papers.nips.cc/paper/5855-a-reduced-dimension-fmri-shared-response-model)

Bibtex:
```
@inproceedings{phchen2015srm,
  title={A Reduced-Dimension f{MRI} Shared Response Model},
  author={Chen, Po-Hsuan and Chen, Janice and Yeshurun, Yaara and Hasson, Uri and Haxby, James V. and Ramadge, Peter J. },
  year={2015},
  booktitle={Advances in Neural Information Processing Systems (NIPS) },
}
```

### To use the code and data please do 
```
# clone code
git clone https://github.com/cameronphchen/SRM.git
# clone data (make sure git-lfs is installed on your machine https://git-lfs.github.com/)
git lfs pull
```

##Code Structure:

Please refer to code/readme.txt for procedure to replicate NIPS results

1. SRM/code:
  * alignment_algo   : alignmetn algorithms
  * experiments      : experiments, called by run_exp*.py
  * plot		       : pipelines for aggregating results and generating figures
  * preprocessing    : preprocessing procedure for each dataset
  * sh_script	       : shell script for running experiments in batch
  * test  		   : testing 
  * transform_matrix : code to match up the testing subject after having template
  * run_exp_imgtrn_mysseg.py : experiment code for training on image testing on mystery segment
  * run_exp_noLR_idvclas.py  : experiment code for group classification
  * run_exp_noLR.py          : experiment code for image prediction and myster segment identification without seperating left and right hemisphere
  * run_exp.py               : experiment code for image prediction and myster segment identification seperating left and right hemisphere

2. SRM/data:
  * In data folder, there should be data/input, data/working, data/output

##Datasets :

*  raider : .mat files in data/input/raider/. If you use this dataset please refer to [Haxby et at. 2011 Neuron](http://haxbylab.dartmouth.edu/publications/HGC+11.pdf).

*  forrest: Please refer to www.studyforrest.org for forrest dataset

*  sherlock and audiobook: The papers for these two datasets are still under review, so the datasets are not available at this point.

SRM

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