NeuralTaskonomy
Inferring the Similarity of Task-Derived Representations from Brain Activity
This repository contains code for the Neural Taskonomy paper, accepted to NeurIPS 2019.
Setup and Installation
Step 1: Clone the code from Github
git clone https://github.com/ariaaay/NeuralTaskonomy.git
cd NeuralTaskonomy
You will also need to clone the taskonomy model bank.
git clone https://github.com/StanfordVL/taskonomy/tree/master/taskbank
BOLD5000 data and stimuli is available for download here.
Step 2: Install requirements
Requirements.txt contains the necessary package for to run the code in this project.
python3 -m venv venv
source venv/bin/activate
pip install -r requirement.txt --no-index
Please also follow installation page to install another environment to use taskonomy model bank.
Step 3: Generate model activations from each task specific models for all BOLD5000 Images
sh scripts/generate_taskonomy_features.sh
To run encoding models (ridge regression) using task presentaitons from all tasks on ROI data and whole brain data.
sh scripts/taskrepr_ROI_train.sh
sh scripts/taskrepr_wholebrain.sh
To run permutatation tests on ROI and whole brain data
sh scripts/taskrepr_ROI_permutation.sh
sh scripts/taskrepr_wholebrain_permutation.sh
This permutates the brain response 5000 times to obtain the null distribution.
To process permutation results:
cd code
python process_permuation_results.py --subj $subj
python make_task_network.py --use_mask_corr --subj $subj --empirical
python run_significance_test.py --subj $subj --whole_brain --use_empirical_p
To generate task similarity matrix:
python make_task_matrix.py --method "cosine" --use_mask_corr --empirical
To generate task trees:
python make_task_tree.py --subj $subj --method masked_corr