Fitting summary statistics of neural data with a differentiable spiking network simulator
Guillaume Bellec*, Shuqi Wang*, Alireza Modirshanechi, Johanni Brea^, Wulfram Gerstner^
Accepted for publication at the NeurIPS conference 2021
(*= equal contributions, ^ = senior authors)
arxiv - NeurIPS proceedings
conda create -n rsnn_SM python=3.8
conda activate rsnn_SM
conda install pip
pip install -r requirements.txt
python train.py --SEED=0 --ground_truth=no --GPU=yes --maxiter=800
The default parameters defined in src.parser.py should match the parameters used in the paper.
To reproduce the results on the V1-dataset, download it at: https://crcns.org/data-sets/vc/pvc-11/about.
Then the instructions to pre-process the data and convert it from .mat to .npy as detailed in the notebook: convert_data_to_npy.ipynb or in the Appendix A of our paper.
The processed data should be saved in the folder data_dir specified in src.parser.py.
The video data should be saved as a numpy array of shape: ( #pixel_y, #pixel_x, #frame ).
The spike train aligned to the video should be saved in the form of numpy array of shape: ( #repetition * #frame, #neuron).
Similar video spike trains should be saved for validating testing sets, they correspond here to different repetitions of the same movie.
Once the .npy file generated, the DataLoader in src.utils.py handles the preprocessing and the data.
Once a model is trained, one can use forcast.py to simulate spike train samples. It should save generated spike trains and draw raster plots as follows:
