Custom build-on for classifying neural data based on https://github.com/neuroailab/lr-identify

Aran Nayebi*, Sanjana Srivastava*, Surya Ganguli, Daniel L. K. Yamins

34th Conference on Neural Information Processing Systems (NeurIPS 2020)

Preprint

Getting started

First clone this repo, then install dependencies

pip install -r requirements.txt

We recommend Python 3.6 if you run the above requirements file.

For users who use older versions of Python, we note that in the original paper:

• We used Python 2.7, so the code is backwards-compatible with this version of Python.
• We used TensorFlow 1.13.1 for all of our model training experiments on TPU and for generating these observable statistics on GPU.
• We used numpy 1.16.3, scipy 1.2.1, and scikit-learn 0.20.4 to train classifiers on these generated observable statistics.

Tutorial

See this Google Colab notebook for a brief tutorial on the use of many parts of this codebase, including analyzing the dataset, visualizing saved classifier results, and training your own classifiers on the dataset.

Downloading the dataset

To download the dataset of generated observable statistics, simply run

./get_dataset.sh

This will save dataset.pkl to the current directory.

Downloading saved classifier results

To download results from pretrained classifiers (Random Forest, Linear SVM, and Conv1D MLP) when trained to separate all four learning rules on ten category-balanced 75%/25% train/test splits of the data using all of the observable statistics, simply run

./get_saved_results.sh

This will save the .pkl files to a new directory called saved_classifier_results/.

TensorFlow implementations

The tensorflow/ folder contains our implementations of models (tensorflow/Models/) and observable statistics (tensorflow/Metrics/functions.py). It is mainly intended to be used for reference, as the code there is not meant to run.

Cite

If you used this dataset or codebase for your research, please consider citing our paper:

@inproceedings{lridentify2020,
    title={Identifying Learning Rules From Neural Network Observables},
    author={Nayebi, Aran and Srivastava, Sanjana and Ganguli, Surya and Yamins, Daniel LK},
    booktitle={The 34th Conference on Neural Information Processing Systems (NeurIPS 2020)},
    url={https://arxiv.org/abs/2010.11765},
    year={2020}
}

Contact

If you have any questions or encounter issues, either submit a Github issue here or email anayebi@stanford.edu.