WASABI

Weighted Adaptive Stimulation After Batch-testing Identification (WASABI)

Overview

This repository contains the simulation code for the identification of stimulation parameters.

Folder structure:

.
├── docs                     # Problem formulation
├── src
│   └── wasabi               # Functions for scripts 
├── scripts                  # Scripts for plotting
├── LICENSE
└── README.md

Outputs:

.
├── figures                      # Output figures
├── results                      # Output files

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Installing the pipeline

Use Anaconda Prompt on windows, make sure you have CUDA installed

*navigate to desired directory*
# 1) clone repo
git clone https://github.com/bryan-tseng/WASABI
cd WASABI

# 2) create environment
conda env create -f environment.yml -n wasabi
conda activate wasabi
python -m pip install --upgrade pip

# 3) install WASABI package in editable mode
pip install -e .

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Details of the pipeline

Outputs

1. Raster plots of first trial
2. Spike times of simulated data (nTrial x nChannel x Time of spikes)

TODOs

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Evaluation

Contact

For questions, contact Bryan Tseng btseng2@jh.edu.

Acknowledgements

Adam S. Charles, Sai Koukuntla.

License