deepSTRF

Fitting of auditory neural responses with deep neural network models

Work in progress - Jan. 2024

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Presentation

This repository is associated with the paper "A general theoretical framework unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses" by Rançon et al.

It contains major codes for result reproduction. In particular, it provides several publicly available datasets that in convenient PyTorch classes, as well as ready-to-deploy computational models and the AdapTrans model of auditory ON/OFF responses.

Details about currently available models and datasets in the doc folder.

Benchmark

To foster improvement of auditory neural encoding models, we report here information about the best-performing models, on each dataset. Feel free to contact us, if you want to claim a spot on the podium of either dataset ! A ready-to-deploy PyTorch model class will have to be provided to support your claim, and facilitate the work of future researchers.

Dataset	Model	Ranking	Note	Parameters	Perfs (CCraw / CCnorm) [%]	Paper
NS1	2D-CNN	🥇	uses AdapTrans	37,276	43.7 / 65.3	Pennington et al.
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Wehr	NRF	🥇	uses AdapTrans	40,261	26.0 / 26.3	Harper et al.
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		🥉
NAT4 A1	2D-CNN	🥇	uses AdapTrans	15,748	32.6 / 56.9	Pennington et al.
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NAT4 PEG	2D-CNN	🥇	uses AdapTrans	15,748	37.2 / 62.4	Pennington et al.
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		🥉

Installation

This repository assumes you are comfortable with Python environments and conda. To learn more about conda, please visit https://anaconda.org/.

First create a conda environment and activate it with the following commands:

conda create --name deepSTRF_env python=3.8
conda activate deepSTRF_env

Then, download this repository and get inside:

git clone https://github.com/urancon/deepSTRF
cd deepSTRF

Install Python libraries and dependencies for this code:

pip3 install -r requirements.txt

Download the datasets and move them to the appropriate place:

Explain how to download and install the datasets ! 

Now you should be all set up to reproduce our experiments. Have fun !

Train a model

We use Weights and Biases for logging during model training. It is a popular tool among deep learning researchers, as it allows to synchronize, plot, and compare metrics for your different runs on a single cloud space, for free. A downside is that it requires an account; please refer to their website for more information.

The script main.py allows you to reproduce major experiments presented in our paper. To train our model with default settings, just execute the following command:

python3 main.py

You can also do your own experiment by changing the hyperparameters ! For instance:

python3 main.py -option1 value1 -option2 value 2

To know more about possible options, please do:

python3 main.py --help

Contributing

In building and maintaining this repository, our goal is to contribute to harmonize the preprocessing of datasets and model training procedures.

We provide some guidelines on the data formats, tensors, models, etc. The automatic differentiation and GPU parallelization enabled by the PyTorch deep learning library makes a good basis for the task of neural response fitting.

If you agree with the open-science philosophy and would like to share your data, you can either contribute to this repository (we would be glad to help you doing so) or make your own with a similar architecture.

References

This work was made possible by the generous publication of several electrophysiology datasets. If you find them useful for your research or use them, please do not forget to cite their corresponding article:

• NS1
• Wehr
• NAT4

Citation

If you use this repository useful for your research and/or liked our paper, please consider citing it in your work:

@article{rancon2024adaptrans,
    author={Rancon, Ulysse and Masquelier, Timothée and Cottereau, Benoit},
    year={2024},
    month={01},
    pages={19},
    title={A general theoretical framework unifying the adaptive, transient and sustained properties of ON and OFF auditory responses},
    publisher = {Cold Spring Harbor Laboratory},
    journal = {bioRxiv},
    doi={10.1101/2024.01.17.576002},
    URL = {https://www.biorxiv.org/content/early/2024/01/20/2024.01.17.576002},
}