Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

This repository is the official implementation of [https://www.biorxiv.org/content/10.1101/2021.10.29.466492v2.full.pdf+html].

Getting started

Disclaimer: Work in progress

We're working on turning the code in this repository into the pip-installable RAVE-toolbox for removing inter-experimental variability from experiments. For the moment being, you can use it to reproduce the figures from our paper by following the instructions below.

1. git clone the repository onto your machine
2. Have a look at the packages listed in the requirements file; make sure installing the packages won't mess with your python installation. Then:
3. From within the directory containing the requirements.txt file, run pip install -r requirements.txt

Downloading the necessary files

You can download the data we worked with from here: https://doi.org/10.12751/g-node.5iije0 In order to reproduce the figures from the paper, you need the following data files:

• Recordings from bipolar cells: bio/...
• simulated bipolar cell responses: silico/...
• IPL info files: ipl/...

Reproducing figures from the paper

To get started, we suggest running the demo notebook for the simulated data, which loads or trains a model with tuned hyperparameters, runs the evaluation functions and creates the corresponding plots. The notebook can be found here: notebooks/Evaluate_sim_data_template.ipynb You need to adjust the file paths in the corresponding section of the notebook to reflect the locations of the downloaded files.