Latent Diffusion for Neural Spiking Data

This repository contains research code for the NeurIPS 2024 Spotlight paper:
Latent Diffusion for Neural Spiking Data
by Kapoor*, Schulz*, Vetter, Pei, Gao†, and Macke† (2024).

Installation

To run the scripts make sure to first install all the requirements. We recommend creating a conda environment first. A GPU is recommend but not necessary.

git clone git@github.com:mackelab/LDNS.git
cd LDNS
conda create --name ldns python=3.9
conda activate ldns
pip install -e . # install from requirements.txt
# optional: install jupyter notebook
pip install jupyter

Downloading human and monkey data

If you want to run the experiments on the human and monkey data, download and store the datasets:

• Monkey data: https://dandiarchive.org/dandiset/000128 (download using dandi download DANDI:000128/0.220113.0400 in data/monkey)
• Human data (Willet et al, 2023): https://datadryad.org/stash/downloads/file_stream/2547369 (download in data/human, then unzip using tar -xvf data/human/2547369)

Running the experiments

The core model and data loading code is in the ldns directory. Training and evaluation is done in .ipynb files in the notebooks directory.

Dataset	Train and Evaluate	Notebook
Lorenz system	autoencoder	notebooks/train_autoencoder_Lorenz.ipynb
	diffusion model	notebooks/train_diffusion_Lorenz.ipynb
Human BCI data	autoencoder	notebooks/train_autoencoder_human.ipynb
	diffusion model	notebooks/train_diffusion_human.ipynb
Monkey reach data	autoencoder	notebooks/train_autoencoder_monkey.ipynb
	diffusion model (unconditional)	notebooks/train_diffusion_monkey.ipynb
	diffusion model (angle-conditioned)	notebooks/train_diffusion_monkey_angle_conditioned.ipynb
	diffusion model (velocity-conditioned)	notebooks/train_diffusion_monkey_velocity_conditioned.ipynb

Note: we are currently cleaning up the notebooks and will update this section soon.

Baselines for the Lorenz and Monkey Reach Data

In the paper, we compared LDNS to a number of VAE-based baselines. The code for these baselines can be found in the baselines directory.

Refactoring and cleaning of the baseline code is in progress. We appreciate your patience!

Citation

@inproceedings{kapoorschulz2024ldns,
	author = {Jaivardhan Kapoor and Auguste Schulz and Julius Vetter and Felix C Pei and Richard Gao and Jakob H. Macke},  
	title = {Latent Diffusion for Neural Spiking Data},  
	journal = {Advances in Neural Information Processing Systems},
	year = {2024}  
}

Contact

Please open a Github issue for any questions, or send an email to jaivardhan.kapoor@uni-tuebingen.de or auguste.schulz@uni-tuebingen.de.